System and method for adverse mobile application identification

ABSTRACT

A system and method identifies mobile applications that can have an adverse effect on a mobile device or mobile network. In an implementation, a server monitors behavioral data relating to a mobile application and applies a model to determine if the application has an adverse effect or has the potential to cause an adverse effect on a mobile device or a network the mobile device may connect to. A mobile device may monitor behavioral data, apply a model to the data, and transmit a disposition to the server. The server may aggregate behavioral data or disposition information from multiple devices. The server may transmit or make available the disposition information to a subscriber through a web interface, API, email, or other mechanism. After identifying that an application may have an adverse effect, the server may enact corrective actions, such as generating device or network configuration data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 12/868,669, entitled SYSTEM AND METHOD FOR SERVER-COUPLEDMALWARE PREVENTION, filed on Aug. 25, 2010, which is a continuation inpart of U.S. patent application Ser. No. 12/255,621, entitled “SYSTEMAND METHOD FOR ATTACK AND MALWARE PREVENTION,” filed on Oct. 21, 2008,and incorporated by reference herein. This application is related to thefollowing U.S. Patent Applications: U.S. patent application Ser. No.12/868,672, entitled “SYSTEM AND METHOD FOR SECURITY DATA COLLECTION ANDANALYSIS,” and U.S. patent application Ser. No. 12/868,676, entitled“SYSTEM AND METHOD FOR MOBILE COMMUNICATION DEVICE APPLICATIONADVISEMENT,” all of which are incorporated by reference herein.

BACKGROUND

This disclosure relates generally to mobile security, and specifically,to detecting and preventing data from adversely affecting a mobilecommunication device or group of mobile communication devices.

Today's mobile communication devices, such as cellular telephones,smartphones, wireless-enabled personal data assistants, tablet PCs,netbooks, and the like, are becoming more common as platforms forvarious software applications. A mobile communication device user nowhas more freedom to choose and install different software applications,thereby customizing the mobile communication device experience. However,while there are many positive software applications available on themarket, the ability to interact, install, and operate third partysoftware inevitably leaves the mobile communication device susceptibleto vulnerabilities, malware, and other harmful software applications.Unlike desktop computers and other less portable computing devices thatcan install and run antivirus software to protect against harmfulsoftware applications, mobile communication devices lack the processingpower or resources for effectively running analogous software.

Third party applications have been developed that provide rudimentaryscanning functions on a mobile communication device; however, theseapplications are often device, operating system, orapplication-specific. As such, a single universal platform-agnosticsystem for efficiently monitoring, scanning, remedying, and protectingmobile communication devices does not exist. It would be desirable toprovide such a system that works on any mobile communication device,that is hardware and software agnostic, and that can be continuouslyupdated to provide constant real-time protection. Moreover, it would bedesirable to provide an adaptable system that can act and react to thedemands and changes affecting a number of mobile communication devices,thereby providing intelligent malware protection.

One feature common to many mobile communication devices is the fact thatthey are constantly connected to a network. However, despite this commonlink, it is difficult to safeguard mobile communication devices fully atthe mobile network level, as devices may connect to additional networksand utilize encrypted services, both of which often bypass network levelprotection. Rather than rely only on the processing and memory resourcesof each mobile communication device on the network, it would bedesirable to provide a system that protects mobile communication devicesremotely, providing malware prevention and analysis measures to multipledevices without the overhead of those measures running locally on eachdevice.

One of the issues that make it difficult to protect mobile communicationdevices from undesirable applications is the many different types ofdata and applications that are available for such devices. While serviceproviders are able to manage the network traffic in providingapplications, there is no current way to effectively monitor thebehavior of these applications after they have been installed on auser's mobile communication device. As a further result, it is difficultto identify new, previously unknown malicious applications by theirbehavior and to track and prevent the spread or dissemination ofdamaging applications and data once they have been released to thenetwork. It would be desirable to provide a system that can activelymonitor a group of mobile communication devices in order gather dataabout the installation and behavior of applications on mobilecommunication devices.

Once such a system is in place, it would be desirable to use data andinformation gained about mobile communication device applications tohelp users make more educated decisions about the applications theychoose to run on their mobile communication devices and to allowadministrators and network operators to take preventative measures tofurther secure both individual devices and the network as a whole. Itwould further desirable to develop a way to anonymously collect dataabout mobile communication device behaviors and activities in order topromote the development of safer mobile applications.

BRIEF DESCRIPTION OF THE FIGURES

This disclosure is illustrated by way of example and not limitation inthe figures of the accompanying drawings, in which like referencesindicate similar elements, and in which:

FIG. 1 is an exemplary block diagram depicting an embodiment of thedisclosure.

FIG. 2A is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 2B is an exemplary block diagram depicting an embodiment of thedisclosure.

FIG. 2C is an exemplary block diagram depicting an embodiment of thedisclosure.

FIG. 2D is an exemplary block diagram depicting an embodiment of thedisclosure.

FIG. 3 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 4 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 5 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 6 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 7 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 8 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 9 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 10 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 11 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

FIG. 12 is an exemplary flow diagram illustrating the steps of anembodiment of the disclosure.

DETAILED DESCRIPTION

A system and method identifies mobile applications that can have anadverse effect on a mobile device or mobile network. In animplementation, a server monitors behavioral data relating to a mobileapplication and applies a model to determine if the application has anadverse effect or has the potential to cause an adverse effect on amobile device or a network the mobile device may connect to. A mobiledevice may monitor behavioral data, apply a model to the data, andtransmit a disposition to the server. The server may aggregatebehavioral data or disposition information from multiple devices. Theserver may transmit or make available the disposition information to asubscriber through a web interface, API, email, or other mechanism.After identifying that an application may have an adverse effect, theserver may enact corrective actions, such as generating device ornetwork configuration data.

A specific implementation is directed to a system and methods for usinga server to provide protection from and removal of undesiredapplications or other data objects that may affect a mobilecommunication device or plurality of mobile communication devices,regardless of the make or model of the mobile communication device(s),the mobile communication network, or the software applications presenton the mobile communication device(s). As used herein, all of theservices associated with the identification, analysis, and removal ofpotentially undesired applications or other data objects, as well asmobile communication device protection are described under thenon-limiting term, “security.” Thus, an embodiment of this disclosure isdirected to providing security to a plurality of mobile communicationdevices, such as a plurality of mobile communication devices for a groupof employees, or a plurality of mobile communication devices that accessa particular network. An embodiment of this disclosure is directed tosafely and securely gathering information about applications on mobilecommunication devices without taxing individual mobile communicationdevices or the mobile network and utilizing the information aboutapplications to secure mobile communication devices. An embodiment ofthis disclosure is directed to using information gathered from mobilecommunication devices to generate user or device information that can beused to develop future products or services for mobile communicationdevices. An embodiment of this disclosure is directed to an earlywarning system to detect if an application is harmful or adverse to themobile communication device, mobile communication device network, orboth based on the application's presence on a small number of devicesbefore the application is on a large number of devices.

In a specific embodiment, application behavioral data from two or moremobile communication devices is received and aggregated. A model isapplied to the data to determine whether or not the application wouldhave an adverse effect on a mobile communication device, network, orboth. The behavioral data may be received at a server so that thedetermination can be made at the server. The aggregated behavioral datamay be received at a mobile device so that the determination can be madeat the mobile device. Upon making the determination, dispositioninformation regarding the determination can be created for notifying asubscriber that the application would have an adverse effect on themobile device, network, or both. Configuration information can begenerated at the server and transmitted to the mobile device, network,or both, to prevent the application from adversely affecting the mobiledevice, network, or both.

It should be appreciated that an embodiment of this disclosure can beimplemented in numerous ways, including as a process, an apparatus, asystem, a device, a method, a computer readable medium such as acomputer readable storage medium containing computer readableinstructions or computer program code, or as a computer program productcomprising a computer usable medium having a computer readable programcode embodied therein. One will appreciate that the mobile communicationdevice described herein may include any computer or computing devicerunning an operating system for use on handheld or mobile devices, suchas smartphones, PDAs, tablets, mobile phones and the like. For example,a mobile communication device may include devices such as the AppleiPhone®, the Apple iPad®, the Palm Pre™, or any device running the AppleiOS™, Android™ OS, Google Chrome OS, Symbian OS®, Windows Mobile® OS,Palm OS® or Palm Web OS™. As used herein, the mobile communicationdevice may also be referred to as a mobile device, a mobile client, orsimply, as a device or as a client.

In the context of this document, a computer usable medium or computerreadable medium may be any medium that can contain or store the programfor use by or in connection with the instruction execution system,apparatus or device. For example, the computer readable storage mediumor computer usable medium may be, but is not limited to, a random accessmemory (RAM), read-only memory (ROM), or a persistent store, such as amass storage device, hard drives, CDROM, DVDROM, tape, erasableprogrammable read-only memory (EPROM or flash memory), or any magnetic,electromagnetic, infrared, optical, or electrical system, apparatus ordevice for storing information. Alternatively or additionally, thecomputer readable storage medium or computer usable medium may be anycombination of these devices or even paper or another suitable mediumupon which the program code is printed, as the program code can beelectronically captured, via, for instance, optical scanning of thepaper or other medium, then compiled, interpreted, or otherwiseprocessed in a suitable manner, if necessary, and then stored in acomputer memory.

Applications, software programs or computer readable instructions may bereferred to as components or modules or data objects or data items.Applications may be hardwired or hard coded in hardware or take the formof software executing on a general purpose computer such that when thesoftware is loaded into and/or executed by the computer, the computerbecomes an apparatus for practicing the disclosure. Applications mayalso be downloaded in whole or in part through the use of a softwaredevelopment kit or toolkit that enables the creation and implementationof an embodiment of the disclosure. In this specification, theseimplementations, or any other form that an embodiment of the disclosuremay take, may be referred to as techniques. In general, the order of thesteps of disclosed processes may be altered within the scope of thedisclosure.

As previously mentioned, security services may be provided to one ormore mobile communication devices by a server or group of servers thatoperate together. There are many possible ways in which multiple serversmay operate together to provide security services without departing fromthe scope of this disclosure. An embodiment of this system is shown inFIG. 1, in which one or more servers 151 communicate with one or moremobile communication devices 101 over a cellular, wireless Internet orother network 121. As mentioned above, mobile communication device 101may also be referred to as a “mobile client device,” “client device,”“device,” or “client,” and may be referred to in the singular or pluralform. The one or more servers 151 may have access to a data storage 111that stores security information for the one or more mobilecommunication devices 101. Data, assessment information, informationabout the mobile communication devices 101, or other objects for storagemay be stored on servers 151 and/or data storage 111. Servers 151 ordata storage 111 may be singular or plural, or may be physical orvirtualized. Data storage 111 may be a database, data table, datastructure, file system or other memory store. Data storage 111 may behosted on any of the one or more servers 151, or may exist externallyfrom the one or more servers 151, so long as the one or more servers 151have access to data storage 111. In an embodiment, data storage 111 isan external service provided by a third-party, such as the SimpleStorage Service (S3) or other products provided by Amazon Web Services,LLC. One will appreciate that the configuration of the systemillustrated in FIG. 1 is non-limiting and merely exemplary, and thatother configurations are possible without departing from thisdisclosure.

One will appreciate that communication between mobile communicationdevice 101 and server 151 may utilize a variety of networking protocolsand security measures. In an embodiment, server 151 operates as an HTTPserver and the device 101 operates as an HTTP client. To secure the datain transit, mobile communication device 101 and server 151 may useTransaction Layer Security (“TLS”). Additionally, to ensure that mobilecommunication device 101 has authority to access server 151, and/or toverify the identity of mobile communication device 101, device 101 maysend one or more identifiers or authentication credentials to server151. For example, authentication credentials may include a user name andpassword, device-specific credentials, or any other data that identifiesmobile communication device 101 to server 151. Authentication may allowserver 151 to store information specific to mobile communication device101 or an account associated with mobile communication device 101, toprovide customized services to device 101, and to maintain a persistentview of the security status of mobile communication device 101.

In order to provide security services for mobile communication device101, one having ordinary skill in the art will appreciate that mobilecommunication device 101 will transmit certain data to server 151. Aswill be discussed in more detail below, server 151 will analyze thisdata and provide a security related assessment, response and/or otheraction. The following describes the type(s) of data transmitted frommobile communication device 101 to server 151, the analysis performed byserver 151, and the action taken with or by mobile communication device101.

One will appreciate that an embodiment of this disclosure may existindependently on mobile communications device 101, or may beincorporated into an existing security system resident in the mobilecommunications device such as the one described in U.S. patentapplication Ser. No. 12/255,614, entitled “SYSTEM AND METHOD FORMONITORING AND ANALYZING MULTIPLE INTERFACES AND MULTIPLE PROTOCOLS,”filed on Oct. 21, 2008, and incorporated in full herein. One havingordinary skill in the art will also appreciate that in order toimplement an embodiment of this disclosure on a variety of mobilecommunications device platforms, it may be necessary to incorporate across-platform system such as the one disclosed in U.S. patentapplication Ser. No. 12/255,626, entitled “SYSTEM AND METHOD FOR AMOBILE CROSS PLATFORM SOFTWARE SYSTEM,” filed on Oct. 21, 2008, andincorporated in full herein. In addition, as discussed further below,aspects of this disclosure may be used to determine a security state fora mobile communications device 101, as described in U.S. patentapplication Ser. No. 12/255,632, entitled “SECURE MOBILE PLATFORMSYSTEM,” filed on Oct. 21, 2008, and incorporated in full herein.

One having ordinary skill in the art will appreciate that mobilecommunication devices are exposed to different types of data. This dataincludes network data, files, executable and non-executableapplications, emails, and other types of objects that can be transmittedto, received by, or installed on a mobile communications device. Mobilecommunication devices also typically transmit and receive data throughone or more network interfaces, including Bluetooth, WiFi, infrared,radio receivers, and the like. Similarly, data may be encapsulated in alayered communications protocol or set of protocols, such as TCP/IP,HTTP, Bluetooth, etc. Current server-client security models, such asthose currently available for desktop and laptop computers, cannotextend their capabilities to provide adequate assessment and security toa plurality of mobile communication devices.

This disclosure contemplates at least two types of data that can be usedto evaluate and protect mobile communication devices. The first type ofdata includes data about a mobile communication device, i.e., “devicedata.” Device data pertains to the state, capabilities, operatingsystem, firmware version, memory capacity, available communicationports, battery limitations, hardware characteristics and other“baseline” information that may be common to all similar devices absentuser customization. Device data may include the default specificationsfor a device as it is received from a manufacturer, service provider, orIT service. Device data may include state information common to allsimilar mobile communications after they have all been upgraded in somefashion. As will be discussed further below, device data may be used toevaluate whether vulnerabilities exist due to unguarded communicationports, operating system exploits, device-specific attacks, and the like.

A second type of data that can be used to evaluate mobile communicationdevices is data that pertains to a particular application, file, orobject that may be installed or run on a mobile communication device. Asused herein, this data is referred to as “application data.” Applicationdata includes both data objects and information about data objects, suchas behavioral data or metadata. Data objects include applicationpackages that may be particular to certain mobile communication devices.For example, iPhone OS devices typically use IPA files or APP packages,Android OS devices typically use APK files, Windows Mobile devicestypically use CAB, EXE or DLL files, and Symbian OS devices typicallyuse SIS files. Devices may also support cross-platform applicationformats such as the SWF format underlying Adobe's Flash runtime or JARfiles that can be run on Java virtual machines.

Application data includes data objects that are malware or spyware, andthereby can negatively affect a mobile communication device. Malware andspyware include applications, files, and other data objects that arepurposefully designed to adversely affect or steal information from amobile communication device. Application data also includes data objectsthat are not designed for nefarious reasons, but may have coding flawsor other issues that can negatively affect a device. Application dataalso includes data objects that may be undesirable for various reasons.For example, a data object may be undesirable because it compromisesprivacy, overtaxes a device's battery or network connection, and/or hasobjectionable content. As used herein, “data objects” may also bereferred to as “data items.” Use of either term is not intended to limitthe data to any one form.

Application data includes metadata about data objects. For example,metadata is information about a specific data object, rather than thedata object itself. Metadata includes the location on a mobilecommunication device's filesystem where a data object is stored, a hashof the data object, the name of the data object, a unique identifierpresent in or associated with the data object such as a GUID or UUID,security information related to the data object such as itscryptographic signer information or level of permissions granted, andcharacteristics of how the data object is installed on or integrateswith the mobile communication device's operating system. Metadata for adata object may also include from where the data object came (e.g., aURL from where it was downloaded, an application marketplace from whichit was downloaded, a memory card from where it was installed or stored.Metadata may also be retrieved from an application marketplace. Suchmetadata, called marketplace metadata, includes information about a dataobject such as the number of downloads, user comments about the dataobject, the description of the data object, permissions requested by thedata object, hardware or software requirements for the data object,information about the data object's author, the price of the dataobject, the language or languages supported by the data object, andother information that a marketplace may provide.

In an embodiment, application data also includes behavioral data.Behavioral data includes information about how an application interactswith or uses a mobile communication device's resources, such as memoryusage, battery usage, network usage, storage usage, CPU usages, APIusage, errors and crashes, network services connected to (e.g., remotehost address and port), and runtime library linkage. Behavioral dataalso includes information about how an application, file or data object,when it is run, utilizes the functionalities of the mobile communicationdevice's operating system, such as notifications and messaging betweenprocesses or installed applications.

As will be explained further below, both device data and applicationdata are useful for providing an assessment of the security of a devicebased upon the data stored (e.g., installed applications) or passingthrough the device. One having ordinary skill in the art will appreciatethat device data and application data are merely examples of the typesof data that may used in order to safeguard a mobile communicationdevice or provide other functions related to a mobile communicationdevice. Other types of data may also be evaluated by the disclosedsystem without departing from the scope of this disclosure. As usedherein, the term assessment refers to information relating to a dataobject that may be used to evaluate or otherwise further understand adata object's operation or effect of operation. For example, anassessment may include a determination that an application is maliciousor non-malicious, bad or good, unsafe or safe, or that an applicationmay appear on a blacklist or whitelist. An assessment may includecategorization or characterization data for a data object, ratings suchas security ratings, privacy ratings, performance ratings, qualityratings, and battery impact ratings for a data object, trust ratings fora data object, distribution data for a data object. Assessments mayresult from collecting and/or processing data by server 151 and may beexposed by server 151 to users or other systems via an API, userinterfaces, data feeds, or other methods. One will appreciate that theprevious description for an “assessment” is not meant to be limiting inany fashion.

A. Device Data Collection, Models, and Remediation

What follows is a discussion about how device data and application dataare collected and stored, according to an embodiment of this disclosure.In general, the following discussion includes communications betweenserver 151 and mobile communication devices 101 over network 121. Anydata transmitted or received during these communications may be storedon server 151 or on data storage 111. In an embodiment, data stored ondata storage 111 or server 151 is associated with a particular accountor device known to the system. The association between data and a deviceor account may allow server 151 to provide tailored functionality forthe account or device based on previously received data. In anembodiment, some or all of the data is stored on server 151 or datastorage 111 with an anonymous association to a particular account ordevice. For example, data may be stored with an anonymous associationfor privacy purposes so that examination of the data on server 151 ordata store 111 cannot tie the anonymously-associated data to aparticular account or device; however, a device can populate and updatethis anonymously-associated data. Anonymous associations are describedin further detail below. In an embodiment, server 151 will requestinformation from mobile communication devices 101, which will respondwith the requested information. In an embodiment, a mobile communicationdevice 101 will transmit device data and/or application data to server151 for analysis and assessment. For example, a user of mobilecommunication device 101 may wish to download a file to his device, butprior to installing the file, may wish to send the file or identifyingdata associated with the file to the server 151 in order to check if thefile is malicious or otherwise undesirable. Server 151 will then analyzethis received information in order to provide a security assessment thatis available to any of the mobile communication devices 101. The server151 can apply a model to at least some of the obtained behavioral datafor the data object. In another example, it may be useful to know how anassessed data object will affect the performance or behavior of a mobilecommunication device, the assessment containing information such asaverage battery impact or average network usage of the data object. Inan embodiment, server 151 stores assessments of data objects afteranalysis and can provide access to these assessments in a number ofways. The analysis performed by server 151 will be discussed furtherbelow. The process by which server 151 provides access to assessmentinformation will be also be discussed further below.

To prevent taxing network 121 and server 151 with network traffic,various methods may be used to reduce the amount of data requested byand transmitted to server 151. For example, rather than transmittingwhole data objects, such as application files or application packages,for analysis, hashing functions or hashing algorithms may be applied todata and the resulting hash of the data may be sent to the server 151.The server 151 may use the hash to uniquely identify the data object. Ifthe server has previously performed an assessment of the data objectidentified by the hash, the server 151 may return that previousassessment if it is still valid. If the server 151 has not yet performedan assessment for the data object, the server 151 may return a responseindicating that the assessment is unknown and/or request additional datafrom the mobile communication device 101. One having ordinary skill inthe art will appreciate that a hashing algorithm will transform anarbitrary amount of data into a fixed length identifier. For example,the SHA-1 hashing algorithm can digest an arbitrary amount of input datainto a 160-bit hash. In another example, metadata besides a hash of thedata object may be sent in lieu of a data object itself, e.g., metadatafor an application may be sent for an assessment rather than the wholeapplication. In many cases, metadata, such as a package name,application name, file name, file size, permissions requested,cryptographic signer, download source, a unique identifier such as aUUID, and other information may be sufficient as identifying informationfor a data object; thus, if server 151 receives appropriate identifyinginformation, it can determine if the data object is undesirable. Oneskilled in the art will appreciate that there are a variety of methodsby which a data object can be identified in such a way that can allowserver 151 to determine if a data object installed on device 101 ismalicious without having to transmit the entire data object to server151.

In an embodiment of this disclosure, server 151 may request portions ofa data object, rather than a complete data object. A whole data objectmay be transmitted incrementally such that network 121 is not burdenedby network traffic. Alternatively or additionally, server 151 mayrequest information about a particular application, but may query agroup of mobile communication devices that each has this application. Inthis manner, server 151 may receive a portion, or “chunk” of data fromone mobile communication device, and another portion of data from asecond mobile communication device, and so forth, as necessary. Server151 may then aggregate this information as it is being received, therebypooling from a number of mobile communication device having theapplication/file data without taxing any specific mobile communicationdevice. An example of this method is discussed further below.

FIG. 2A is a general overview of the transmission of different types ofdata between a mobile communication device 101 and server 151. As FIG.2A shows, in block 201, mobile communication device 101 sendsapplication data to server 151, which receives this data (block 203). Inthis embodiment, mobile communication device sends identifying orauthentication information to server 151 so that server 151 canreference previously stored identifying or authentication informationabout mobile communication device 101, store and retrieve dataassociated with the mobile communication device 101, and specificallyidentify or authenticate mobile communication device 101 amongst othermobile communication devices.

In an embodiment, server 151 sends a notification to mobilecommunication device 101 (block 205). This notification can be an alert,a message, an instruction or other information related to applicationdata or device data specific to mobile communication device 101. In anembodiment, the notification is due to the device previously having sentapplication data corresponding to a data object that was not initiallyassessed by the server 151 to be undesirable but was subsequentlydetermined by the server 151 to be undesirable. In block 207, mobilecommunication device 101 receives the notification, and in block 209,the mobile communication device 101 takes action based upon thenotification. As will be discussed in more detail below, such actionsmay include deactivating one or more features or applications on themobile communication device 101.

One having skill in the art will appreciate that the interaction betweenmobile communication device 101 and server 151 can include communicationfrom the mobile communication device to the server, as well as from theserver to the mobile communication device. For example, in anembodiment, server 151 may receive application data from mobilecommunication device 101, but server 151 may require additionalinformation before providing an assessment or transmitting anotification. In block 211, server 151 may request the additionalinformation from mobile communication device 101. Mobile communicationdevice receives the request (block 213), gathers additional informationas requested by server 151 (block 215), then in block 217, transmits theadditional information to server 151. In block 219, server 151 receivesthe requested additional information. One will appreciate that thisprocess may repeat as necessary.

In an embodiment, the server 151 is in communication with a plurality ofmobile communication devices 101 operating in a mobile communicationdevice network. The server 151 monitors behavioral data for a dataobject accessed by at least one mobile communication device. Thebehavioral data is stored in a data store accessible by the server 151.When it is making an assessment of the data object, the server 151accesses the stored behavioral data and applies a model to at least someportion of the stored data. The purpose of the model is to determinewhether or not the data object would have an adverse effect on the atleast one mobile communication device network, or at least one mobilecommunication device. If the application of the model to at least aportion of the behavioral data indicates that the data object would havean adverse impact upon the mobile communication device or the mobilecommunication device network, the server creates disposition information(relating to an assessment of the data object) that can be stored andcommunicated to system subscribers who want to be informed about dataobjects that will adversely effect either the mobile communicationdevice (specific one or specific device type) or a mobile communicationdevice network.

In this embodiment, the behavioral data can be correlated to mobilecommunication device data for at least one of the mobile communicationdevices that accessed the data object. The disposition information canbe sent to the subscriber as a data feed, an e-mail, a text message, orit can be published as a web interface accessible to the subscriber.

More specifically, FIG. 2B shows a block diagram of a specificembodiment of a system for analyzing behavioral data gathered from themobile communication devices. The system accepts as input behavioraldata 250 from one or more client devices 101 and outputs aggregatedbehavioral data result 253, disposition information 254, or both.

In particular, as shown in FIG. 2B, a monitoring program 256 is at theclient. The client may include any number of application programs suchas application program A, application program B, and so forth which aremonitored by the monitoring program. Some examples of applicationprograms which the monitoring program may monitor include Bump®,Facebook®, Foursquare®, Geodelic®, Goggles, Layar®, and many others.

In this specific embodiment, the server includes an aggregation engine258 and a determination engine 260. The determination engine includesmodels such as model A, model B, and model C.

The monitoring program at the client transmits to the server behavioraldata based on the monitoring of the one or more application programs atthe client. In a specific embodiment, the behavioral data is inputted tothe determination engine as individual behavioral data. That is, theinput of the behavioral data is non-aggregated behavioral data, i.e., isfrom a single client or single application program on the client.

The determination engine, upon receiving the behavioral data applies amodel to the behavioral data to determine whether the applicationprogram associated with the behavioral data would have an adverse effecton the client, the network, or both. For example, network operators canuse the information provided by the system to detect applications thatare causing problems on the user client devices, to detect deviceincompatibilities with particular applications, and to detectapplications that may adversely affect mobile network performance oravailability. Enterprises may use the information to determine whatservice is acceptable. Further, the information may be used to determinemalfunctioning devices.

As an example, model A may include a policy that specifies the thresholdlimit for network usage is a rate of 100 megabytes per day. If thebehavioral data indicates that the application's network usage is abovethis threshold limit then the application can be flagged as adverselyaffecting the network or having the potential to adversely affect thenetwork.

As another example, a given cell network may be able to handle 20megabits per second of downstream data transfer (i.e. data downloaded toa mobile communication device). If a single application constantly uses5 megabits per second, a single user would not cause an adverse effect,though 5 users would. The system can identify the potential adverseeffect upon the first user using the application program, rather thanwaiting for the network to actually be adversely effected. Thus, in anetwork adversity analysis, the device or application behavior itself isnot adverse, but the behavior is a characteristic, which, if widelydeployed, would be problematic. In another example, the rate at which anapplication causes data connections on a mobile communication devicenetwork to be opened may be used to determine adverse behavior. In aspecific embodiment, the connection rate (i.e., how often does theapplication program attempt to transfer data over the network) is usedto detect if the application program is harmful or adverse. In anembodiment, the connection rate determination takes into accountinformation such as the packet size, duration, and frequency of anapplication's network data transfers that can be used to determinewhether or not an application causes state transitions of a mobilecommunication device's cellular network radio. For example, if anapplication transmits or receives network data in a manner that causesan undesirable number of radio state transitions on a cellular radio,that application may be considered harmful or adverse. In anotherspecific embodiment, the connection rate is used in combination withother behavioral data such as the amount of data transmitted andreceived by the application program, the number of mobile communicationdevices on which the application is installed, and so forth.

Alternatively, the system can identify the actual adverse effect ratherthan the potential to have such an effect given the behavioral data. Asa further example, for adverse effects that are contained on the device(e.g., battery overuse, crashes, slowness, or sluggish applicationresponse), the system can detect a potential adverse effect based on thebehavioral data (e.g., using more battery than typical applications,high CPU utilization) or detect the actual adverse effect (e.g., thedevice running out of battery, crashes occurring, UI waitingnotifications).

The output of the system can include disposition information (e.g., adetermination that a particular application may have an adverse effecton the network, client, or both). In a specific embodiment, thedisposition information includes an adverseness score 265 of theapplication program. For example, an adverseness score value of 80 mayindicate that the application is very likely to be adverse. In contrast,a lower adverseness score value, such as 20, may indicate that theapplication is less likely to be adverse.

The adverseness score may indicate a degree of adverseness orintrusiveness. For example, a score of 95 may indicate that theapplication program is very intrusive (e.g., application program tracksthe client's precise location using a global positioning system (GPS) ormobile network and sends or transmits the location off the client; orthe application program has access to information that can be used toidentify the user of the client including the user's mobile number andclient serial number).

The disposition information may instead or additionally include anadverse/non-adverse result 267, i.e., a binary result that indicateswhether the application program is adverse or not adverse. Thedisposition information may be generated automatically based on theanalysis of the behavioral data.

The disposition information can be made available to system users by anynumber of techniques. In an embodiment, the information is published ona website such as a website maintained by the system or a third-partywebsite (e.g., Facebook®). A user can access the website or webpage toview the information, download the information, or both. In one specificembodiment, the information is publicly available on the website. Inanother specific embodiment, the disposition information or at least aportion of the disposition information is not publicly available. Forexample, the user may need to login to the website via a username andpassword. In another embodiment, the user may also have to be a servicesubscriber.

In another specific embodiment, the disposition information istransmitted from the server to a user such as via e-mail, text message,a tweet via Twitter®, a data feed (e.g., Really Simple Syndication(RSS)), or combinations of these in order to notify the user. In anotherspecific embodiment, an application programming interface (API) with orwithout an API key required is provided so that other software servicescan access and use the information provided by the system.

A user who receives or is given access to the disposition informationmay be referred to as a subscriber. A subscriber does not necessarilyneed to have the application referenced in the disposition informationinstalled on a mobile communication device. For example, the subscribermay be interested in keeping abreast of trends in applicationdevelopment, regardless of whether or not the subscriber has theapplication. In a specific embodiment, subscribing to the dispositioninformation includes making a payment. The payment may be a one-timepayment, a monthly payment, an annual payment, and so forth.

In another specific embodiment, the subscription is without cost to theuser, but the user must complete a signup process where the user entersinformation such as their name and e-mail address. As part of thedisposition information subscription process, the user may be asked tocomplete a marketing survey. The survey can request information such asthe user's age, birthday, address, what products the user typicallyuses, what websites the user typically visits, or combinations of these.There can be a promotional period in which the disposition informationis provided without charge, but afterwards payment is required in orderto continue to receive the disposition information. Alternatively, thedisposition information or a portion of the disposition information maybe provided as a free, publicly available service. Dispositioninformation provided without charge may be accompanied by advertisementssuch as banner ads embedded with the disposition information.

A software system that registers to receive information about one ormore applications may also be referred to as a subscriber or asubscriber agent. In other words, a subscriber may be software or asoftware program (e.g., executable code) that communicates with theserver via an API. In an embodiment, the subscriber can register toreceive information about all applications or about specificapplications. For example, the server may, by default, notify asubscriber (e.g. a user or software system) about any applications thatare considered to be adverse, but allow configuration of a subset ofapplications for the subscriber to subscribe to. This configuration orpreferences information may be stored at the server or client. Thus, afirst subscriber may be notified by the server about a first set ofapplications considered to be adverse, based on the configuration orpreferences information of the first subscriber. A second subscriber maybe notified by the server about a second set of applications consideredto be adverse, based on the configuration or preferences information ofthe second subscriber, where the second set of applications is differentfrom the first set of applications. Allowing subscribers to choose theapplications that they want to receive information about helps to ensurethat the subscribers are not overloaded with information. A subscribercan register to receive information about a specific application, acategory of applications (e.g., games, entertainment, news,productivity, search tools, social networking, or sports), applicationsfrom a specific developer or company, or combinations of these.

Generally, the disposition information, behavioral data, post-aggregateddata (e.g., aggregated behavioral data and aggregated determinations),other intermediate output, or any combinations of these is saved orstored on server 151 (or at a storage location accessible by the server)so that the data can be accessed at a later time. For example, it may bedesirable to later access the data to perform a statistical analysis orother studies or analyses. Such studies or analyses may be based on dataaccumulated over a period of several months or years. The informationmay be saved or stored in nonvolatile memory or other persistent storagemedium (e.g., hard disk, optical disc, flash memory, and so forth).

In another specific embodiment, the behavioral data is aggregated by theaggregation engine before the data is received as input to thedetermination engine. In a specific embodiment, the aggregatedbehavioral data is from two or more different client devices, i.e.,multiple devices. In this specific embodiment, a model operates on thebehavioral data from the two or more different client devices, i.e., theaggregated behavioral data. The aggregated behavioral data may be storedusing a variety of data stores such as in a file or database table. Forexample, the data may be stored in a purpose-built binary file format orusing a database system such as SQLite, MySQL™, or HBase. Theaggregation engine can add behavioral data from first and second clientdevices to a table. Table A below shows an example of aggregatedbehavioral data.

TABLE A Network Usage Rate Client Application Program (megabytes perday) 1 Application Program A 125 2 Application Program A 105 3Application Program A 130 4 Application Program B 20 5 ApplicationProgram B 10 6 Application Program A 90 7 Application Program B 25

As shown in the example table A above, the table specifies theapplication program installed at the client and behavioral dataassociated with the application program. That is, the behavioral data iscorrelated to the client device that accessed the application program.In this example, the behavioral data includes an indication of thenetwork usage of the application program. For example, client 1 includesapplication program A where the network usage rate by applicationprogram A at client 1 is 125 megabytes per day. Client 2 includesapplication program A also where the network usage rate by applicationprogram A at client 2 is 100 megabytes per day, and so forth. Thenetwork usage rate may be an average or rolling average that iscalculated over a specific period of time (e.g., 1 week, 2 weeks, 1month, 10 days, 1 hour, or 8 hours). For example, the network usage rateof 125 megabytes per day for application program A at client 1 may be anaverage based on the application's daily usage of the network over a oneweek period. The specific period of time may be consecutive units oftime or nonconsecutive units of time (e.g., 10 business days).

In this specific embodiment, the determination engine takes as input theaggregated behavioral data and applies a model to make determinations ofadverseness. The model can specify, for example, that if 10 percent ormore of the time a particular application is installed it uses more than100 megabytes per day, then it is considered potentially adverse.

For example, analyzing application program A via the model may includeidentifying a number of installs of the application program. In thisexample, application program A is installed on clients 1, 2, 3, and 6.So, there is a total of 4 installs of application program A. As shown intable A above, application program A's network usage rate exceeded 100megabytes per day at clients 1, 2, and 3. So, there is a total of 3instances or occasions where application program A's network usage rateexceeded 100 megabytes per day. Thus, the percent of time theapplication (or percent of application A installs) where the usage rateexceeded 100 megabytes per day is 75 percent (i.e., 3 instancesexceeding 100 megabytes per day divided by 4 total installs equals 75percent). Thus, application program A according to the model isdetermined to have or potentially have adverse effects because 75percent of the time the application program is installed it uses morethan 100 megabytes per day.

In contrast, as shown above in table A, the network usage rate forapplication program B falls well below the 10 percent/100 megabytes perday threshold limitations. So, application program B would not beflagged as being adverse.

Thus, the disposition information indicates that application program Ais (or has the potential to be) adverse. The information or indicationmay be used to notify a network administrator so that the administratorcan examine the outputted aggregate behavioral data more closely. Thedisposition information may indicate that application program B is notadverse.

Table A above showed an example where network usage rate was calculatedas a daily rate. However, it should be appreciated that the usage ratemay instead be calculated on a more granular level such as per minute,per hour, or for a particular time period during the day such as 10:00am to 11:00 am. The determination engine can analyze such aggregatedbehavioral data to characterize an application as potentially adversebased on factors such as the number of instances, installations, ordownloads of the application program, the network usage rate for aspecific time period, rate of open connections, and so forth. Thedetermination engine can scan or traverse the aggregated data andcorrelate such factors to identify relationships between two or morefactors to make determinations of adverseness.

For example, an application program that had a moderate network usagerate but whose usage was concentrated during a particular time period(e.g., 10:00 am to 11:00 am) may be characterized as adverse if there isa very high number installations. Thus, even though the application'snetwork usage rate is moderate, the high number of installations anddata indicating that most of the users use the application around thesame time period can result in the application program beingcharacterized as adverse. In contrast, an application program that had avery high network usage rate, but whose usage was scattered throughoutthe day or occurred during off-peak hours, may not be characterized asadverse because usage of the application is unlikely to degrade thenetwork.

Generally, to characterize an application program, a model is applied toat least a portion of the behavioral data collected from one or moreclient devices, such as two or more different client devices. The modelspecifies which specific collected behavioral data points of anapplication program should be analyzed to determine whether theapplication program will be characterized as adverse. Examples ofcollected behavioral data include information indicating network usage,number of open connections, amount of time the user spent using theapplication program, what time during the day the application programwas used, what other application programs are on the device, what otherapplication programs were being concurrently executed, user id, orinformation about the sensitive actions performed by the applicationprogram (e.g., accessing GPS unit of device, application accessing adirectory of contacts stored on device, application accessing deviceconfiguration information, application accessing system registry files,or application accessing personal user information stored on device). Amodel can use any combination of this behavioral data in order tocharacterize an application program. Further, when characterizing anapplication program, a model may use in combination with behavioral dataother data as well such as evaluations or ratings of the applicationprogram from other sources.

In a specific embodiment, the determination engine analyzes rawbehavioral data. In another specific embodiment, the aggregation engineprocesses or preprocesses the behavioral data before a model is appliedto the aggregated behavioral data. In a specific embodiment, theprocessing includes generating a data distribution, probability curve,average (e.g., average battery consumption across two or more devices),rolling average, weighted average, ratios, or some other statistical ornon-statistical calculation, or combinations of these. Thus, forexample, a model of the determination engine may operate on a normalizedform of the data such as a histogram rather than the raw behavioraldata.

A data distribution can identify the number of devices that reported avariable as X, the number of devices that reported a variable as Y, andso forth. In a specific embodiment, a data distribution is generatedbased on device battery consumption. The data distribution calculationcan indicate the number of occurrences or frequency at which anapplication program was found to have consumed certain amounts ofbattery.

The behavioral data collected at the client may be transmitted to theserver by any number of techniques. In a specific embodiment, thebehavioral data is transmitted from the client to the server based on apredetermined schedule such as during off-peak hours to reduce load onthe network. Alternatively, the behavioral data may be transmitted inreal-time. In an embodiment, the frequency at which behavioral data istransmitted is based on the release date of the application, developerof the application, or both. For new applications, behavioral data maybe sent more frequently as compared to old applications since there islikely to be more uncertainty with new applications. Certain applicationdevelopers may be identified as consistently developing applicationsthat are abusive. So, in an embodiment, applications from thesedevelopers will have their behavioral data sent more frequently so as tokeep a closer watch on these applications.

Behavioral data may be transmitted using a push-model from the client tothe server. Alternatively, the data may be transmitted using apull-model in which the server requests the data from the client. In anembodiment, the system employs statistical techniques to gatherbehavioral data of a representative sample or a statisticallysignificant number of the devices. Limiting the amount of behavioraldata to a representative sample as compared to gathering data from everysingle device helps to reduce network load. Alternatively, data may begathered from every single device. Further details of behavioral datacollection is described in the discussion below that accompanies FIG. 7.

As shown in FIG. 2B, in another specific embodiment, determinations ofadverseness from the determination engine are inputted to theaggregation engine to be aggregated 270. It may be desirable todetermine if an application's adverseness on a particular device beforedetermining if the application is generally considered adverse. Forexample, a model for determining adverseness may need to take intoaccount multiple data sources relative to a particular device ratherthan operating on aggregate data. Aggregating the determinations canallow for an overall determination of adverseness of an applicationprogram based on the number of times the application program is flaggedby the determination engine. Specifically, if there are 100installations of an application program, but the application program wasflagged only 2 times by the determination engine then the applicationprogram may not be categorized as being adverse. But, if the applicationprogram was flagged 80 times by the determination engine, theapplication program may be categorized as being adverse.

In some cases, determinations of adverseness are based on multiplefactors (e.g., peak data rate, average data rate, or data transfer) thatmay be interrelated. There can be any number of factors, e.g., 1, 2, 3,4, or more than 4 factors. These factors determine the characterizationof an application program as adverse or not adverse. In cases wheremultiple factors are involved it may be desirable to make adetermination before an aggregation is performed to reduce thecomplexity of the calculations, and to reduce the probability that somedata will be lost such as through averaging out and rounding errors.Thus, analyzing behavioral data of a single device can help to ensuregranularity. After a determination is made for the single device or theapplication on the device, the determinations can be aggregated toobtain a macro view or overall determination of whether or not anapplication should be characterized as adverse.

In another specific embodiment, the aggregation engine aggregatesbehavioral data from two or more application programs on a singledevice. Table B below shows an example of aggregating behavioral datafrom two or more applications on a single client device where theaggregated behavioral data includes an indication of the amount ofbattery consumption by the application program.

TABLE B Rate of Battery Consumption Application Program (milliwatt-hoursper hour) Application Program A 100 Application Program B 90 ApplicationProgram C 125 Application Program D 60 Application Program E 150

As shown in the example of table B above, application program A consumesa battery of a client device at a rate of 100 milliwatt-hours per hour,application program B consumes the battery at a rate of 90milliwatt-hours per hour, and so forth. Because it may not be possibleto directly measure the battery consumption of a particular applicationon a mobile communication device, it may be desirable to estimate thebattery consumption based on measurable characteristics. In anembodiment, battery consumption is estimated based on general ordevice-specific battery consumption in relation to measurablecharacteristics. For example, measurable characteristics may include anapplication's CPU time, GPS usage time, data transfer over particularnetwork types, network traffic (e.g., amount of data or connection rateinformation for data that the application program receives, transmits,or both over the network), and any modification to the device's powerstate. One will appreciate that the battery consumption estimation maytake place in a variety of places, such as part of a determinationengine, part of gathering behavioral data, or part of an aggregationengine.

The aggregated behavioral data can be inputted to the determinationengine so that the determination engine can apply a model to determinewhich application program on the client consumes the most batterycapacity. In this example, the model identifies application program E asconsuming the most battery capacity as compared to application programsA, B, C, and D. A user of the client device may then receive a warningmessage from the system that application program E consumes a very largeamount of battery capacity as compared to other applications on theclient device. Thus, the user will know that when using applicationprogram E, the application program will deplete the device's batterylife faster than when using the other application programs and the usercan tailor their use of the application program appropriately.

In another specific embodiment, a model includes a points system todetermine whether the application program will have an adverse effect.Application program activities or operations are assigned a specificnumber of points. When the number of points an application programaccumulates exceeds a threshold limit the application program isdetermined to have an adverse effect on the network, mobilecommunication device, or both.

For example, a first rule of the model may specify that each megabyte ofnetwork usage per day is 1 point. A second rule of the model may specifythat each connection per minute is 2 points. A third rule of the modelmay specify that when the total number of points an application programaccumulates is greater than a threshold limit of 100 points, theapplication program is determined to have an adverse effect.

In this example, a first input to the model is the application's networkusage such as 90 megabytes per day which is assessed 90 points based onthe first rule. A second input to the model is the number of connectionssuch as 10 connections per minute which is assessed 20 points based onthe second rule. The sum of the points is 110 points (90 points+20points=110 points). So, based on the third rule the application programis determined to have an adverse effect because 110 points is greaterthan the 100 point limit specified in the third rule. Thus, in thisexample, a combination of network data usage and a total number ofconnections are inputs into a model that determines if an applicationhas an adverse effect.

In another specific embodiment, separate models may be applied fordifferent network types. In this specific embodiment, model B is usedfor Code Division Multiple Access (CDMA) networks. Model C is used forGlobal System for Mobile Communications (GSM) networks. Models B and Cmay have different network usage rate thresholds, different connectionrate thresholds, or both which trigger a determination of whether theapplication program will have an adverse effect. For example, model Bmay be applied where the device data indicates a CDMA network. Model Bmay categorize application programs that use more than 100 megabytes perday as having an adverse effect. Model C may be applied where the devicedata indicates a GSM network. Model C may categorize applicationprograms that make more than 500 connections per hour as having anadverse effect.

Some networks may be more sensitive to certain types of applicationprogram operations as compared to other networks. Thus, selecting whichmodel to apply based on the type of network can be desirable because ithelps to determine the adverseness of an application on a particularnetwork type. Similarly, selecting which model to apply may be based onthe type of client device, carrier, or both. For example, carrier A(e.g., AT&T) may specify network usage thresholds that are differentfrom another carrier B (e.g., Verizon). In an embodiment, if a mobilecommunication device is capable of operating on multiple network types(e.g., General Packet Radio Service (GPRS) and Wi-Fi), behavioral datafor an application differentiates network behavior based on network typeso that separate models may be applied for different network types. Forexample, if a network-intensive application only makes large datatransfers over Wi-Fi, then a model for determining adverseness of theapplication on a GPRS network would only specify thresholds for theportion of the application's network traffic that is occurs on a GPRSnetwork.

In an embodiment, the threshold limits of the models such as networkdata usage and number of connections are user-configurable. That is, anadministrator can configure data usage thresholds, connectionthresholds, or both that, when exceeded, will consider or determine theexceeding application abusive.

In a specific embodiment, the system includes a fuzzy logic system thattakes multiple types of network usage metrics to produce an adversenessrating because it may be desirable to have a more granular assessment ofadverseness rather than a simple binary result. For example, the systemmay take as inputs the application's average data transmission rate, theapplication's highest amount of data transmitted in a minute, theapplication's average connection rate, and the application's highestnumber of connections in a minute. One skilled in the art willappreciate that the system may use a variety of techniques to produce afuzzy adverseness rating. For example, the model may have an equationthat determines a fuzzy adverseness rating from the inputs or the modelmay use data resulting from machine learning, such as a series ofmembership functions that, when applied to a given set up inputs,produce a fuzzy adverseness rating.

FIG. 2B shows three models, however, it should be appreciated that therecan be any number of models. A model can represent the relationships andinterdependencies among the variables which can affect the network andcan be used to determine whether a particular application is likely orunlikely to adversely affect the network. A model can include one ormore rules having a conditional statement and action, e.g., if X then doY, else do Z, an application policy that includes a behaviorallimitation such as a threshold limit on the rate of open connections, orboth. A model can be used to simulate network effects, performforecasting, or a what-if analysis based on the aggregated behavioraldata. In a specific embodiment, a network administrator can alter aportion of the aggregated behavioral data to create a what-if scenariofor application of the model. The output of the model allows theadministrator to see how alterations or changes in behavior are likelyto affect the network. Some examples of changing the behavioral data forpredictive modeling include increasing or decreasing the number of usersusing an application program, increasing or decreasing the number ofusers using an application program during a specific time period, and soforth. This feature allows network administrators to be able to predictthe probability of an outcome and ensure that the network remainsoperational.

The aggregated behavioral data exposed by the server or outputted isreferred to as aggregated behavioral data result 253. The result dataincludes the data which led the system to determine an application'sadverseness (e.g., application transmits an average of 100 megabits ofdata per minute and opens 10 connections per second). In variousembodiments, the data is made available as raw data, a web page, an APIresponse, provided as a report, or combinations of these. Generally, theformat can be in any externally consumable format that can bemachine-readable or readable by a human. For example, the result may beprovided as a report (e.g., pdf report, or printed report) for a networkadministrator. The report may include text, metrics, graphs, tables, orcharts. The report can help the administrator to identify thoseapplications that use the most network resources. In such cases, theadministrator may seek to start charging or impose additional costs forthe use of those applications.

The administrator may be associated with a specific network carrier(e.g., AT&T or Verizon). The aggregated behavioral data result and thedisposition information help to provide an early warning to suchadministrators so that the administrators can take corrective action ifneeded. Such action can include contacting the application developer,developing a partnership with the developer, pulling or removing theapplication program from the marketplace (e.g., Android marketplace),blocking the application from running on the devices, developing aresponse plan to ensure that if additional users download theapplication that the network will not be adversely affected (e.g., lossof a cell tower), or combinations of these. The information provided bythe system can allow carrier network administrators to identify whichapplications are slowing down the network, devices, or both. Further,the information can be used to identify bugs in the devices that causean application to malfunction. If the information indicates that aspecific application is especially popular, the carrier can work withthe developer to optimize the application for the carrier's devices.

FIG. 2C shows a block diagram of another specific embodiment of a systemfor analyzing behavioral data. FIG. 2C is similar to FIG. 2B, but inFIG. 2C a determination engine 276 is at the client device whereas inFIG. 2B, the determination engine is at the server. The implementationin FIG. 2B may be referred to as a server-side implementation of thedetermination engine. The implementation in FIG. 2C may be referred toas a client-side implementation of the determination engine.

Having the determination engine on the client device allows adetermination of whether or not an application is adverse or abusive tobe made on the client device. This can be useful in cases where, forexample, the user is unable to connect to the network, the determinationengine uses a large amount of data from the device that is undesirableto transmit over the network, there is a large amount of complexbehavioral data such that it would desirable to preprocess or make aninitial determination before transmitting the data to the server, orcombinations of these.

As shown in FIG. 2C, the determination engine at the client makesdeterminations of adverseness based on local behavioral data 250associated with one or more application programs at the client. Thedetermination engine may additionally receive aggregated behavioral data252 from a remote source, such as server 151. As discussed above, theaggregated data includes behavioral data from other client devices. Thereceived aggregated behavioral data provides additional data points forthe client-based determination engine which can improve the accuracy ofdetermining whether or not an application program is adverse. Using thelocal behavioral data, the client-based determination engine can make apreliminary determination of adverseness. Upon receiving the aggregatedbehavioral data, the client-based determination engine may alter itspreliminary determination. Alternatively, the received aggregatedbehavioral data may be combined with the local behavioral data and thecombined behavioral data is analyzed by the determination engine at theclient.

After the determination engine makes a determination at the client, thedetermination can be transmitted to the server for aggregation. That is,the aggregation engine at the server can receive determinations ofadverseness from multiple client devices and then aggregate the receiveddeterminations. The determination made at the client device may insteador additionally be made available as disposition information. Thedisposition information may be made available at the client device wherethe determination was made, at a device other than the client devicewhere the determination was made (e.g., at the client device of anetwork administrator), or both.

Determination engine 276 may utilize a fewer number of models thandetermination engine 260 (FIG. 2B), such as only models that arerelevant to the client device, carrier network of the client device,applications on the client device, or combinations of these. Having afew number of models can reduce processing overhead.

FIG. 2D shows a block diagram of remediation upon determining that anapplication program may have or has an adverse effect on the network,client device, or both. As shown in FIG. 2D, for device remediation,device configuration information 278 is generated at the server and istransmitted from the server to one or more client devices such as clientdevice 101, a client device 280, or both. The device configurationinformation may be used by the monitoring program to block or restrictthe normal operations of an application program that is determined bythe system to be abusive.

In the case of network remediation, the server generates networkconfiguration information or network infrastructure configurationinformation 282. The information may be transmitted to infrastructurepowering network 121 so that the network may be directly configured. Forexample, the information may be transmitted to one or more networkdevices such as a firewall, intrusion prevention system, proxy, router,switch, and so forth. The information may instead or additionally bemade available for download by a network administrator 285. For example,network infrastructure configuration information may be used to blocktraffic associated with the application program, block transmission ofan application binary for the application program, or both.

Configuration information may be referred to as a configuration profile,configuration file, or configuration settings.

Device configuration information may be transmitted from the server tothe client that has the abusive application program to prevent adverseeffects to the client, network, or both. Configuration information maybe transmitted to a client that does not have the application program asa preventative measure. For example, the configuration information maycontain network addresses associated with an adverse application toblock traffic to and from so that the application does not function onthe network. In this example, traffic associated with an adverseapplication may be blocked on a cellular network but allowed on a Wi-Finetwork. In another example, the configuration information may contain asignature to block the application binary from being transmitted acrossthe network, a URL associated with the application to block, orinstructions to an application market to block the application frombeing downloaded. In this example, the application itself may beprevented from being downloaded or used.

FIGS. 3-7 illustrate the transmission and collection of application dataand device data in more detail. FIG. 3 illustrates an embodiment inwhich server 151 evaluates a change in a data object stored on mobilecommunication device 101. In FIG. 3, mobile communication device 101detects a change in a specific data object (block 301). One having skillin the art will appreciate that detecting changes in a data object mayinvolve mechanisms such as intercepting system calls or file systemoperations, a file system or other data object change listener,receiving an event from a package management system (e.g.,PACKAGE_UPDATED and/or PACKAGE_REPLACED intents in the Android™operating system), and polling for data objects in a file system orother system capable of enumerating data objects. Other techniques fordetecting changes may also be used. Alternatively or additionally, thefollowing methods may occur when a change to a data object is detected,upon request by the user of the mobile communication device, or upon apre-configured schedule for analyzing and assessing data objects on themobile communication device.

In an embodiment, a change in a data object includes any time a dataobject is added, removed, or modified. After transmitting applicationdata for a data object, mobile communication device 101 waits forconfirmation from the server before recording that it has successfullytransmitted application data for the data object. After receivingapplication data for a data object from a mobile communication device101, server 151 transmits a confirmation. If there was an error intransmission or with the data itself, server 151 returns an error. Ifmobile communication device 101 receives an error from server 151, or noresponse after transmitting application data for a data object, mobilecommunication device 101 will not record the application data for thedata object as having been sent, and the mobile communication device 101may retry sending the data at some point in the future. One skilled inthe art will recognize that mobile communication devices are sometimesunable to connect to a network or may have their network connectioninterrupted in the middle of a transmission. As such, a mobilecommunication device 101 recording whether or not server 151 hassuccessfully received application data for a data object is important tothe functioning of a reliable data collection system. In an embodiment,any time application data for a data object has not been transmittedfrom mobile communication device 101 and received by server 151, it isconsidered to be changed and needs to be transmitted.

In an embodiment, mobile communication device 101 stores whether it hastransmitted and server 151 has successfully received application datafor one or more data objects present on the device. In order to identifywhich data objects have had appropriate application data reported toserver 151, mobile communication device 101 may store a databasecontaining identification information for data objects that have beensuccessfully reported to server 151 to determine whether the deviceneeds to transmit application data for those data objects. For example,a data object that is a file on a filesystem may be identified by a hashof its contents. When the data object is first installed on a mobilecommunication device 101, the database may contain no data for the dataobject. Because there is no identifying information for the data object,the mobile communication device 101 recognizes the data object as newand transmits application data for the data object to server 151indicating that the object is new. After transmitting application datafor the data object to server 151 and receiving confirmation that theserver successfully received the application data, the device stores thehash of the file contents and the location on the filesystem where thefile resides in the database. If the data object were to be deleted, themobile communication device 101 can detect that there is no file at thepreviously stored filesystem location and can report the deletion of thedata object to server 151 by reporting the filesystem location and/orhash identification information for the data object. If the file were tobe modified, such as in the case of an application being updated, themobile communication device can detect that there is a file in thepreviously stored location on the filesystem, but the content hash ofthe file does not match the stored content hash. In this case, themobile communication device 101 can report to the server that the dataobject identified by the file location and/or previous content hash hasbeen updated and report the new content hash of the file.

In an example, a security system installed on mobile communicationdevice 101 may report application data for a data object to server 151for purposes of receiving an assessment of the data object. If a mobilecommunication device downloads a new application that is malicious, itis important that the security system detect this new item as soon aspossible. Server 151 can analyze the new application and provide asecurity assessment whereby actions can be taken based on the results.In another example, a first version of an application may be safe, but asecond version of the application may be malicious. It is important thata security system recognize this update as different from the firstversion of the application so that it will produce a new assessment ofthe second version and not just report the first assessment. Server 151can analyze the updated application and provide a security assessmentwhereby actions can be taken based on the results.

In block 303 of FIG. 3, mobile communication device 101 transmitsidentification information for the mobile communication device to server151. In an embodiment, the identification information is authenticationinformation. In an embodiment, the identification information is anon-authoritative identifier for the device such as a device ID that isnot considered to be secret. In an embodiment, identificationinformation includes device information for the mobile communicationdevice (e.g., make, model, hardware characteristics). In addition,mobile communication device 101 transmits information for the changeddata object. Such information may include identifying information forthe data object, such as metadata (e.g., hash, package name, file name,file path, cryptographic signer, unique identifier such as a UUID) andthe like. In block 305, server 151 receives the identifier for mobilecommunication device 101 and information for the changed data object.The received data is stored by server 151 on the server or on datastorage 111 (block 307). In an embodiment, only some of the datareceived by server 151 is stored. In block 309, server 151 provides anassessment for the changed data object using any of the techniquesdisclosed herein or from U.S. patent application Ser. No. 12/255,621,which is incorporated in full herein. The assessment may includeinstructions and/or a categorization labeling the changed data object assafe, malicious, or unknown. In an embodiment, some or all of thereceived data is stored on server 151 or data storage 111 and isassociated with the device that transmitted the data. For example, thismay later allow server 151 to determine which applications a device hasencountered. In another embodiment, some or all of the received data isstored on server 151 or data storage 111 in a way that server cannotdirectly tie the information to a particular device. For example, server151 may store received data without any link to a particular device oraccount. In another example, data may be anonymously associated with adevice by the server associating the data with an identifier whenstored. To ensure that server 151 cannot associate the stored data witha particular device, the identifier is only known to the devicetransmitting the data and is provided to the server whenever the devicetransmits data. The server does not store this identifier so that theidentifier is never directly linked with a particular device or accounton server 151 or data store 111. In an embodiment, server 151 stores theresults of the assessment on the server or on data storage 111. If, whenan assessment for a data object is required 309 and a previousassessment for the data object exists and is considered valid, server151 retrieves the previous assessment from data storage 111 instead ofperforming a new assessment. Assessments may be considered to be for thesame data object if the metadata relating to each object matches in avariety of ways, including if the assessments relate to data objectswith the same hash, same package name, same cryptographic signer, orsame file path. In block 311, the assessment is transmitted to mobilecommunication device 101, which receives this assessment from server 151(block 313), then processes the assessment or takes appropriate action(block 315).

One having ordinary skill in the art will appreciate that theinteraction between mobile communication device 101 and server 151 isdynamic, in that server 151 can proactively transmit notifications orinstructions to remediate data objects whose assessment has changed,thereby requiring action by mobile communication device 101. FIG. 4illustrates such an embodiment. In block 401 of FIG. 4, mobilecommunication device 101 detects a change in a specific data object. Inblock 403, mobile communication device 101 sends identificationinformation for the device and information about the changed data objectto server 151. Server 151 receives the identification information formobile communication device 101 and information about the changed dataobject (block 405). In block 407, server 151 stores the changed datainformation on the server or on data storage 111. In block 409, server151 may analyze and assess the changed data object, and may report theassessment to mobile communication device 101 (block 411). As discussedpreviously, if an assessment has already been performed for the dataobject, that previously performed assessment may be retrieved and usedinstead of re-performing the assessment. If server 151 reports anassessment, mobile communication device 101 receives the assessment orother notification in block 413, and processes the assessment (block415).

In an embodiment, the assessment for the data object may change. Forexample, a data object that may previously have been assessed as safe orunknown may later be identified as malicious, causing some previouslyunknown vulnerability, or causing an undesirable behavior such asnetwork overuse or battery drainage. In block 417, if server 151 detectsa change in assessment for a previously analyzed data object, then inblock 419, server 151 may transmit a notification, remediationinstructions or the like to mobile communication device 101. Mobilecommunication device 101 receives the notification from server 151(block 421), then performs the recommended actions or remediationinstructions (block 423). In block 425, mobile communication device 101transmits a confirmation that it performed the required actions, whichserver 151 receives (block 427). In an embodiment, the notification isonly sent to mobile communication device 151 if the data object isdetermined to be present on mobile communication device. In anembodiment, the server 151 stores information on the server 151 or ondata storage 111 allowing the server 151 to determine whether the mobilecommunication device 101 currently has the data object or has previouslyrequested an assessment for the data object.

One having skill in the art will appreciate that FIG. 4 provides onlyone example of how server 151 may report changes in assessment to amobile communication device, and some steps may be skipped withoutdeparting from this disclosure. For example, mobile communication devicemay perform remediation instructions or other required actions withoutsending confirmation to server 151.

In an embodiment, server 151 may request additional information about aparticular data object from mobile communication device 101. Forexample, mobile communication device 101 may send information about achanged data object to server 151; however, the information sent may beinsufficient for server 151 to perform a conclusive analysis. FIG. 5illustrates this embodiment. In block 501 of FIG. 5, mobilecommunication device 101 detects that a data object has changed, andtransmits identification information for mobile communication device 101with information for the changed data object to server 151 (block 503).Server 151 receives the identification information for mobilecommunication device 101 and information for the changed data object(block 505), and stores the information for the changed data object onthe server or on data storage 111 (block 507). In block 509, server 151determines whether it requires additional information about the changeddata object. For example, server 151 may attempt to assess whether thechanged data object is safe or malicious, but is unable to provide aconclusive assessment (i.e., the assessment results in “unknown”). Thedetermination of whether more information is needed can be performedeither before the server 151 performs an assessment if there is notenough data to even begin an assessment or after an assessment returnsinconclusively due wholly or in part to a lack of data. If additionalinformation is required, then server 151 may request the additionalinformation from mobile communication device 101 (block 511).

In block 513 of FIG. 5, mobile communication device 101 receives therequest for additional information, gathers the requested information(block 515), then transmits the additional information to server 151(block 517). In an embodiment, additional information includesbehavioral data for a data object and application data for the dataobject, such as the content for the data object. In block 519, server151 receives the additional information from mobile communication device101, and stores the additional information (block 521). Server 151 maythen analyze the changed data object information with the additionalinformation to provide an assessment (block 523), which may be sent tothe mobile communication device 101 (block 525). In block 527, mobilecommunication device 101 receives the assessment of the changed dataobject from server 151 then processes the assessment (block 529).

In an embodiment, mobile communication device 101 may elect to transmitadditional information to server 151. For example, server 151 mayanalyze a data object, but not provide a conclusive assessment. Ratherthan requesting additional information from mobile communication device101, the device may request an additional assessment by providingadditional information for the data object to server 151. FIG. 6illustrates this embodiment.

In block 601 of FIG. 6, mobile communication device 101 detects a changein a data object, then in block 603, mobile communication device 101sends its identification information and information for the changeddata object to server 151. In block 605, server 151 receives theidentification information for mobile communication device 101 and theinformation for the changed data object. This information is stored byserver 151 on the server or on data storage 111 (block 607), thenanalyzed by server 151 to result in an assessment (block 609). In block611, server 151 transmits the assessment or an appropriate notificationto mobile communication device 101. Mobile communication device 101receives the assessment from server 151 (block 613 of FIG. 6). In block615, mobile communication device 101 determines whether to sendadditional information about the data object. For example, server 151may be unable to produce an assessment for the data object given thedata it has available, and thus needs more information to be able toproduce an assessment. In block 617, if mobile communication device 101determines that it should send additional information about the dataobject, then this information is gathered. In block 619, mobilecommunication device 101 transmits the additional information to server151, which receives this information (block 621), and stores thereceived additional information (block 623). One will appreciate thatserver 151 will know that the additional information will pertain to theinformation previously received by server 151 (block 605), since mobilecommunication device 101 will transmit identification information withthe additional information.

In block 625 of FIG. 6, server 151 analyzes the additional informationreceived from the mobile communication device 101. In an embodiment, theadditional information may be analyzed with the previously receivedinformation (block 605). In block 627, server 151 transmits theassessment to mobile communication device 101, which processes theassessment (block 629). If mobile communication device 101 still needsto send additional information, it may repeat the process as necessary.

As noted previously, server 151 may have access to a plurality of mobilecommunication devices, some of which may run or store the sameapplication programs or data objects. Requesting data object informationfrom a single mobile communication device can cause network traffic,affecting not only the single mobile communication device, but otherdevices on the network. In an embodiment, if server 151 requiresinformation about a data object that is stored on more than one mobilecommunication device, server 151 can gather portions of the requiredinformation from each of the mobile communication devices, rather thanrelying on a single device. FIG. 7 illustrates an embodiment using afirst and a second mobile communication device, thereby optimizing datacollection from two or more mobile communication devices.

In block 701 of FIG. 7, the first mobile communication device detects achange in a data object. The data object is also found on the secondmobile communication device, but may or may not realize the same change.The first mobile communication device transmits its identificationinformation and information for its changed data object to server 151(block 703). In block 705, server 151 receives the identificationinformation for the first mobile communication device with theinformation for the changed data object. This information is stored byserver 151 (block 709). In block 711, server, 151 determines that itrequires additional information about the data object. In block 713,server 151 identifies the second mobile communication device that server151 knows also stores the data object as well as additional informationfor the data object.

In block 715 of FIG. 7, server 151 requests the additional informationfor the data object from the second mobile communication device. Thisrequest is received by the second mobile communication device (block717). In response, the second mobile communication device will gatherthe additional information (block 719), then transmit the additionalinformation to server 151 (block 721). Server 151 receives (block 723)and stores the additional information about the data object from thesecond mobile communication device on server 151 or on data storage 111(block 725), then analyzes this additional information with thepreviously received information from the first mobile communicationdevice to render an assessment (block 727). This assessment istransmitted to the first mobile communication device (block 729), whichreceives the assessment (block 731) and process the assessment (block733). One will appreciate that if relevant, server 151 may also transmitthe assessment to the second mobile communication device.

In an embodiment, server 151 can gather additional information frommultiple devices. In an embodiment, server 151 chooses which devices torequest additional from by analyzing device information and applicationdata previously stored by server. For example, to characterize anapplication's usage of SMS messaging to determine whether or not it isabusing SMS for spam purposes, server 151 may request the count of SMSmessages sent by an application from many mobile communication devicesthat have previously reported that they have installed the application.In an embodiment, server attempts to analyze a data object to produce anassessment without first waiting to receive information about the dataobject from a device. Instead, server may receive data from othersources and proactively request information from one or more devices tocreate an assessment for the data object.

In an embodiment, application data for a data object that is gatheredand transmitted by mobile communication device 101 to server 151 mayinclude behavioral data about the data object. Usage of such data byserver 151, such as during analysis, is discussed more in depth below.Behavioral data may include information about what the data object didwhen it ran on the device. Examples of behavioral data includeinformation about network connections caused by the data object (e.g.,server names, source/destination addresses and ports, duration ofconnection, connection protocols, amount of data transmitted andreceived, total number of connections, frequency of connections, andnetwork interface information for the connection, DNS requests made),behavior of the data object when run (e.g., system calls, API calls,libraries used, inter-process communication calls, number of SMSmessages transmitted, number of email messages sent, information aboutuser interfaces displayed, URLs accessed), overhead caused by the dataobject (e.g., battery used, CPU time used, network data transmitted,storage used, memory used). Other behavioral data includes the contextwhen a particular behavior occurred (e.g., whether the phone's screenwas off when the data object sent an SMS message, whether the user wasusing the data object when it connected to a remote server, etc.).

Because a large amount behavioral data is generated by data objectsevery time they run, it is important for a mobile communication devicenot to gather or transmit all of the possible behavioral data;otherwise, the gathering and transmission of behavioral data mayover-utilize resources on the device 101, server 151, and the network121. In an embodiment, mobile communication device 101 limits what typeof behavioral data for a data object it gathers and transmits, and howfrequently to gather and transmit behavioral data based on the period oftime since the data object has last changed. For example, when a dataobject is first installed on a mobile communication device, the devicemay gather and transmit the full amount of behavioral data availableevery day. After one week following installation of the data object, thedevice may only send a limited subset of behavioral data in weeklyintervals. A month after installation, the device may only send aminimal amount of behavioral data in monthly intervals. In anembodiment, if the data object were to be updated (e.g., updating anapplication to a different version), the device may transmit the fullscope of behavioral data daily and reduce the scope and frequency ofdata gathered and transmitted after one week and/or after one month. Inan embodiment, server 151 sends configuration to mobile communicationdevice 101 requesting that the device send specific types of behavioraldata at a specific frequency. The device stores the configuration sothat it may determine whether to gather and/or transmit behavioral datafor data objects. In an embodiment, the configuration information isspecific to a particular data object. In an embodiment, theconfiguration information is for all data objects encountered by thedevice. In an embodiment, server 151 requests behavioral data for aparticular data object from the device so that the server can minimizeunnecessarily gathered and transmitted behavioral data.

In an embodiment server 151 can influence the gathering and transmissionof behavioral data from device 101 to server 151. For example, server151 may transmit instructions to mobile communication device 101,requesting behavioral data for a data object only if the server hasinformation indicating that the device currently has the data object,and if the server needs more behavioral data to better assess the dataobject. In an embodiment, the server 151 determines that it needs morebehavioral data for an object based on the number of devices that havealready reported behavioral data. For example, the server may require atleast one hundred (100) devices to report behavioral data for each dataobject in order to have a confident assessment. In an embodiment, thedifference of the behavioral data reported by different devices is usedto determine how much behavioral data is needed for an assessment to beconfident. For example, if thirty (30) devices all reported batteryusage by a data object within a small variance, the server may notrequest any more behavioral data for that object; however, if thosethirty (30) devices showed a wide variation of battery usage, the servermay request behavioral data from two hundred (200) devices.

In an embodiment, a mobile communication device may only transmitbehavioral data if the data is outside of normal bounds. In anembodiment, the bounds are universal to all data objects. For example, abound on network usage may be set so that mobile communication devicetransmits behavioral data for a data object's network connections onlyif the data object maintains at least one open connection for more than50% of the time it is running or if the data object transmits more thanone megabyte of data in a 24 hour period. In an embodiment, server 151can update bounds on a mobile communication device 101 by transmittingupdated bound information to the device. In an embodiment, bounds may beparticular to one or more data objects. For example, a device may have aset of default bounds by which it will send behavioral data, but theserver may transmit bounds for a particular data object, identifyingthat data object through identifying information such as a hash,cryptographic signer, package name, or filesystem location. The updatedbounds may instruct the device to send more or less behavioral data thanthe default set of bounds. For example, a mobile communication devicemay default to never send behavioral data. When a new data object isinstalled on the device, the device reports the installation event andmetadata associated with the data object to the server. If the serverhas already characterized the data object through behavioral data fromother devices, the server may send bounds to the device specifying thetypical behavior of the data object on other devices (e.g., uses lessthan 100 kilobytes of data per day, never sends SMS messages, neversends email) so that if the data object deviates from these bounds, themobile communication device will send the deviated behavioral data tothe server. Such deviations may be useful in the case of a legitimateapplication that becomes exploited and begins exhibitinguncharacteristic behavior or in the case of a “time-bomb” applicationthat only starts becoming malicious after a certain time.

In an embodiment, data transmitted from mobile communication device 101to server 151 is configurable in order to protect user privacy; preventoveruse of device, network, or server resources; or for other reasons.Some example configurations include choosing what application data issent from device 101 to server 151, how often application data is sent,and how application data is re-transmitted should initial transmissionsfail. Example configurations may further include transmitting onlyidentifying information (e.g., no additional metadata or behavioraldata), never transmitting any application data, never transmitting dataobject content, only transmitting application data for data objectsbased on the source of the data objects, only transmitting certain typeof behavioral data, only transmitting a certain amount of applicationdata per day, only transmitting one data object's content per day,transmitting behavioral data a maximum of once per day per data object,and the like. One skilled in the art will recognize that additionalconfigurations are possible without departing from the scope of thedisclosure. In an embodiment, the configuration may be enforced by amobile device 101 and/or server 151 by the device only making certaintransmissions and/or the server only making certain requests from thedevice. In an embodiment, the configuration is controlled by one or moreparties. For example, the configuration may be automatically set byserver 151 or software residing on mobile communication device 101, orcontrolled by an administrator via server 151, and/or controlled by auser via mobile device 101. In an embodiment, portions of theconfiguration are controlled by different parties. For example, a usermay be able to control whether or not data objects are reported toserver 151 but an administrator on server 151 may control the behavioraldata reporting frequency for all devices to optimize battery usage ofthe security system.

In an embodiment, software on a mobile communication device 101 displaysa user interface dialog when it receives a request to transmitapplication data for a data object, such as its content or behavioraldata. As discussed above, a request for the data object's content may befor the whole content or for a portion of the content, the requestidentifying which portion of the content if a portion is requested. Theuser interface dialog displayed may identify the data object for whichapplication data is to be transmitted, and give the device's user achance to allow or reject the transmission. In an embodiment, the dialogallows the user to have the device remember his or her decision forfuture data objects. In an embodiment, the dialog allows the user toview more in-depth information about the application data to be sent,and provides a way for the user to understand the privacy implicationsof sending the data such as linking to a privacy policy, privacydescription, or other content that describes how the data istransmitted, stored, and used. In an embodiment, a mobile communicationdevice attempts to transmit a data object when it receives an indicationthat server 151 needs more information to produce an assessment. In thisinstance, the device may display a user interface dialog prompting thedevice's user to choose whether or not to transmit the data object'scontent when the device attempts to transmit a data object. In anembodiment, some attempted transmission of certain types of applicationdata, such as a data object's content, results in user interface dialogfor confirmation while other types of application data, such as metadataor behavioral data, are transmitted without requiring a userconfirmation.

Because a particular application may utilize multiple data objects, itmay be desirable for mobile communication device 101 and/or server 151to group multiple data objects together so that the application can beanalyzed as a whole. In an embodiment, mobile communication device 101or server 151 may perform grouping by comparing application data betweenmultiple data objects. For example, application data that may be used togroup data objects includes how data objects were installed (e.g., dataobjects from the same installer may be grouped), if data objects arelinked together at runtime or dynamically, whether multiple data objectsare in the same filesystem directory, and if data objects share acryptographic signer. For example, an application installer may extractan executable and multiple libraries to the filesystem on a mobilecommunication device. The mobile communication device 101 may use thecommon installer to consider the data objects grouped and may store thegrouping information for use in gathering behavioral data (discussedbelow). In order for server 151 to recognize the group, each dataobject's application data may include identification information for thecommon installer. The server 151 may explicitly store the groupedrelationship on server 151 or in data storage 111 to efficiently accessthe grouping information during analysis.

Because behavioral data cannot always be attributed to a single dataobject when multiple objects execute together such as in the context ofsingle process, if the device operating system does not support granularbehavioral data, or through other mechanisms, it may be desirable formobile communication device 101 to group multiple data objects togetherand report behavioral data for the group together. In an embodiment,mobile communication device 101 transmits information indicating thatgrouped data objects are associated and transmits application data forgrouped data objects to server 151 together. For example, if a processon a mobile communication loads multiple components from differentvendors and network data can only be gathered on a per-process level,and/or if the process is detected to be connecting to a known maliciousserver, then it may be desirable for all components loaded in theprocess to be identifiable by the server to determine the offendingcomponent. When the mobile communication device 101 gathers behavioraldata (such as the IP addresses the process has connected to) for theprocess, the device reports identification information for all of thedata objects that are associated with the process to the server. Whenthe server receives behavioral data for a group of data objects it mayanalyze behavioral data from multiple devices and determine that onlygroups containing a particular data object will connect to the maliciousserver. Thus, only the data object that results in connecting to themalicious server will be considered malicious. In an embodiment, if amobile communication device does not provide granular information aboutthe behavior of particular data objects, behavioral data for the deviceas a whole may be transmitted to the server as representing the group ofall data objects installed on the device. For example, if an operatingsystem does not provide per-process battery usage information, devicesrunning that operating system may transmit a list of applicationsinstalled on each device and the overall battery life for each device toserver 151. The server can then perform analysis on this data todetermine which applications are correlated to better or worse batterylife and estimate each application's contribution to battery life wheninstalled on a device. In an embodiment where multiple data objects in agroup have different behavioral data gathering configurations, themobile communication device will join the configurations together. Forexample, if mobile communication device 101 is configured to report alarge amount of behavioral data every day for one data object, but isconfigured to only report anomalous behavioral data for another dataobject, and the data objects are grouped, the device may join the twoconfigurations and report a large amount of behavioral data for thegroup. Alternatively, if the second data object is configured to neverreport behavioral data for privacy reasons, no behavioral data may bereported for the group to satisfy the privacy constraint.

One having skill in the art will appreciate that data transmitted byserver 151 or mobile communication device 101, such as metadata,behavioral data, configuration information, behavioral data bounds,grouping data, requests for additional data, notifications, and otherforms of data may be formatted using binary formats or non-binaryformats. Examples include formatting data in XML, JSON, or as part of aURI. The data may be transmitted using a variety of protocols, includingTCP, UDP, DNS, and HTTP. Other formats and/or protocols may be usedwithout departing from this disclosure.

The above are various non-limiting examples of how data is gathered andcollected from one or more mobile communication devices. Techniques foroptimizing data collection are also disclosed above. As discussed,mobile communication devices 101 will transmit some or all of theabove-described data to server 151 for analysis so that server 151 canprovide an assessment of the analyzed data. The following sectiondescribes non-limiting examples of analysis techniques. One having skillin the art will appreciate that while the examples and disclosure belowuses the data gathered using the methods described herein, other typesof data may be transmitted and that this disclosure is not limited tothe data described herein.

B. Data Collection System

One skilled in the art will appreciate that server 151 may receive datafrom sources other than mobile communication devices for use inanalyzing a data object and producing assessments. FIG. 10 illustratesan embodiment in which server 151 may receive data from multiple sourcesand transmit assessment information for multiple uses. One or moreservers 151 are illustrated as a “cloud” to emphasize that multipleservers may operate in coordination to provide the functionalitydisclosed herein. One or more mobile communication devices 101 areillustrated as a group to emphasize that multiple devices 101 maytransmit and receive information to and from server 151. As disclosedabove, one or more mobile communication devices 101 may transmitapplication data for data objects to server 151 and devices 101 mayreceive assessment data, requests for more information, notifications,and the like from server 151.

In addition to gathering data from mobile communication devices, server151 can receive information pertaining to data objects from a variety ofdata gathering systems. Such systems may be separate from server 151 ormay be part of server 151. In an embodiment, a data gathering systemdirectly updates a database or other storage on server 151 or datastorage 111 with information for one or more data objects. In anembodiment, a data gathering system communicates with server 151 toprovide information to server 151. There are many types of systems thatmay be used as data feeds to server 151. Some examples include webcrawlers 1003, application marketplace data gathering systems 1005,honeypots, and other systems that may feed information related to mobiledevice applications to server 151.

In an embodiment, a web crawler 1003 downloads data objects that can runon mobile communication devices and retrieves information about dataobjects, feeding both to server 151. For example, the web crawler 1003may utilize a search engine to look for web sites that host mobileapplications. Once the crawler 1003 identifies sites hosting mobiledownloads, the crawler may retrieve web pages available on those sites,examining the content of each page to determine additional pages toretrieve. For example, a page on a mobile download site may containlinks to other pages as well as links to download data objects. It maybe desirable for data gathering systems to only transmit information toserver 151 that is relevant to mobile devices, as there is much contentavailable on the internet that does not affect mobile communicationdevices (e.g., PC software). In an embodiment, the crawler 1003 canidentify if a data object available for download or that has alreadybeen downloaded is able to run on a mobile communication device. Forexample, the crawler 1003 may examine a download URL for a specificstring indicating that the URL corresponds to mobile application package(e.g., SIS, APK, CAB, IPA). In another example, the crawler 1003 mayexamine a data object after it has been downloaded to determine if itaffects mobile communication devices and if so, whether it affects aspecific mobile platform. In this case, the crawler 1003 may examine thedata object downloaded for characteristics such as its name, whether itcontains executable code compatible with any mobile platforms, or if itcontains data that is typical for a particular mobile device platform.In an embodiment, the web crawler 1003 gathers marketplace metadataabout data items and transmits the marketplace metadata to server 151.Some example marketplace metadata includes from which web sites a dataobject is available for download, user ratings and comments for a dataobject, the price of the data object if it is available for purchase,the number of times the data object has been downloaded, informationabout the author of the data object, and other information pertaining toa data object that is available on web sites. As will be discussedbelow, where a given data object is available can be used to determinehow trustworthy a data object is. For example, a data object availablefrom a reputable company's web site may be considered more trustworthythan a data object uploaded on a mobile device forum by one of theforum's users.

Because many mobile applications are only available via mobileapplication marketplaces, it may be important for server 151 to receiveinformation about data objects that are available in applicationmarketplaces. In an embodiment, an application marketplace datagathering system 1005 retrieves information about a data object, such asthe data object's content and marketplace metadata for the data object,from mobile application marketplaces and reports the information toserver 151. In an embodiment, the application marketplace data gatheringsystem 1005 is part of server 151. In alternative embodiment, theapplication marketplace data gathering system is separate from server151. Application marketplaces are often provided by mobile platformvendors (e.g., Android Marketplace, Blackberry App World, Apple AppStore, Nokia Ovi Store) or third parties (e.g., GetJar, Handango) andmay use a proprietary API. In an embodiment, application marketplacedata gathering system 1005 is configured to communicate with applicationmarketplace servers via a proprietary protocol. In order to transmit thedata received from application marketplace servers to server 151 in amanner that is usable by server 151, the marketplace data gatheringsystem 1005 may transform application data for data objects from aproprietary format into a format that server 151 can utilize foranalysis. For example, an application marketplace may provide an API toaccess users' comments and ratings for an application; however, the datareturned by that API may be different from another applicationmarketplace's comment data. In another example, an application marketmay proactively transmit data to marketplace data gathering system 1005so that the data gathering system does not have to repeatedly query it.To allow server 151 to be able to analyze comment data from multipleapplication marketplaces, application marketplace data gathering system1005 may transform differently formatted comment data into a standardformat for transmission to server 151. In an embodiment, an applicationmarketplace data gathering system 1005 can search for certain terms inuser reviews, such as “battery drain,” “crash,” “privacy settings,”“does not work,” “phone number,” “contacts,” and the like, which can beused to characterize an application as “known bad,” or used to establishthe trustworthiness of an application using the system componentsdescribed herein. In an alternative embodiment, application marketplacedata gathering system 1005 can gather all comment data and analysis ofthe comment data can be performed by server 151. Similarly, server 151or application marketplace data gathering system 1005 can be capable ofrecognizing positive reviews or scores for a data object, therebyimproving the assessment and/or trustworthiness for the data object.

In addition to automated gathering of data object information, it may beimportant for server 151 to accept human information 1007. Suchinformation may include subjective trust scores for mobile applicationvendors, specific keywords or other characteristics, such as heuristics,that may classify a mobile application as suspicious. One skilled in theart will recognize that other types of information related to theanalysis of data objects for mobile devices may be provided by a humanis possible without departing from the scope of this disclosure. In anembodiment, server 151 provides a user interface by which someone mayprovide information to server 151 about a specific data object, a groupof data objects (e.g., data objects from a particular developer, alldata objects on a specific platform), or for the analysis system as awhole (e.g., updated analysis heuristics). In an embodiment, a serverseparate from server 151 provides a user interface by which someone mayprovide information about a specific data object, a group of dataobjects, or for the analysis system as a whole. This separate server maytransmit the user-provided information to server 151 where server 151stores it on server 151 or in data storage 111. In an embodiment, theseparate server directly updates data storage 111 with the user-providedinformation.

FIG. 10 illustrates how server 151 may provide information about dataobjects to external systems. In an embodiment, information provided byserver 151 may be transmitted via an API; provided as a list, a datafeed, a report, or formatted data such as firewall or virus definitions;or in other forms. In an embodiment, server 151 provides informationabout data objects to an application marketplace 1009. For example,server 151 may provide marketplace 1009 with a list of malicious dataobjects that are present in marketplace 1009. In another example, server151 may expose an API by which application marketplace 1009 can transmitidentification information (e.g., a hash of a data object's content) toserver 151 to determine if the data object is considered malicious orotherwise undesirable. In an embodiment, server 151 provides data tonetwork security infrastructure 1011 so that the network securityinfrastructure 1011 may protect against malicious or undesiredapplications at the network level. For example, by protecting at thenetwork level, even mobile communication devices that do not havesecurity software installed may benefit from protection. In anembodiment, server 151 transmits threat signatures to network securityinfrastructure 1011. Such threat signatures may take a variety of forms,for example, hashes of undesired applications, binary sequences forundesired applications, package names of undesired applications,firewall rules to block malicious servers or attackers, and rules for anetwork security system such as Snort. In an embodiment, server 151provides data in the form of data feeds 1013. The data feeds 1013 maycontain a variety of data available to server 151 or data storage 11either from server's data gathering or from further analysis (describedbelow), for example, a list of any data objects that use more networktraffic than a given threshold to identify misbehaving or abusiveapplications, a list of the most prevalent malicious data objects, and alist of applications that match criteria such as a set of heuristics foridentifying potentially malicious applications.

C. Server-Side Analysis Systems

In order to produce assessments for data objects or other forms ofuseful output, server may use a variety of methods of analysis. In anembodiment, because server has access to information collected aboutdata objects from one or more sources, server can process theinformation to produce an assessment for a data object. FIG. 11illustrates an embodiment in which server 151 aggregates applicationdata for a data object, stores the information, generatescharacterizations and categorizations for the data object, assesses thedata object to produce assessment information, and transmits theassessment information. In block 1101 of FIG. 11, application data(e.g., data object content, metadata, behavioral data, marketplacemetadata) is gathered for a data object. Some of the possible methodsfor gathering and types of data gathered have been discussed above. Suchmethods may include gathering data from devices, from web sites, fromapplication marketplaces, from people, and from other sources. In block1103, application data for the data object is stored on server 151 ordata storage 111 so that the data may be used at a different time thanwhen it is gathered.

In block 1105, device data is gathered and stored (block 1107) on server151 or data storage 111. It may be desirable for device data to belinked to the application data for the device that reported so thatassessments, categorization, and characterization can take into accountthe source of the data. For example, if an application only malfunctionswhen installed on a particular device type, it is important for server151 to be able analyze application data provided by devices in thecontext of what particular device type provided the data. In anembodiment, when application data is stored 1103 it is associated withdevice data for the device that provided it. For example, when a device101 transmits application data to server 151, the device may transmitauthentication information that allows server 151 to retrieve previouslystored data for the device 101. If the device 101 has alreadytransmitted device data to server 151, the previously stored device datacan then be associated with the new application data. In such a datagathering system, it may be important to protect privacy and minimizeindividually identifiable information stored by server 151 or datastorage 111. In an embodiment, application data for multiple deviceshaving the same device data is aggregated so that the stored data is notlinked to a particular device, but rather a set of device data shared byone or more devices. In the design of such a system, it may be importantto take into account the balance between granularity of device data andthe level to which the aggregated data can be ascribed to a particulardevice.

As part of analyzing a data object, it may be desirable for server 151to characterize it and/or categorize it (block 1109). In an embodiment,server 151 stores characterization and categorization data for dataobjects (block 1111). It may be desirable for characterization andcategorization data to be updated as more data becomes available oranalysis of the data changes. In an embodiment, server 151 performsadditional analysis (block 1109) and updates stored categorization andcharacterization data (block 1111) for a data object when new or updateddata for the data object used by analysis systems is available.

Characterization data includes information that describes a dataobject's functionality, behavior, and reputation such as itscapabilities, metrics for the data object, analyses of other datarelating to the data object, and the like. In an embodiment, server 151produces characterization data about a data object using applicationdata, device data, marketplace data, distribution data, and other dataavailable to server 151. While some methods are described below, oneskilled in the art will appreciate that there are other of methods forgenerating characterization information that can be employed withoutdeparting from the scope of this disclosure. In an embodiment, server151 transmits characterization information as an assessment. One willappreciate that characterization information may be useful for a user tounderstand when deciding whether to install an application. For example,if a user is considering downloading a game but the user receives anassessment indicating that the game has the capability to send theuser's location to the internet, the user may decide not to install thegame. In another example, if a user is considering downloading aninstant messaging application and is concerned that the application mayuse a disproportionate amount of battery power, the user may receive anassessment to see the application's average battery usage metric anddecide that, based on the metric, the application is acceptable toinstall. In an embodiment, characterization information is consumed asan input to one or more other analysis systems. For example, an analysissystem producing an assessment of the privacy risk of an application mayuse characterization information to determine if an application hasrisky capabilities such as sending location or contact list informationto an internet server.

Capabilities are one form of characterization data that server 151 mayproduce. In an embodiment, server 151 extracts capabilities from a dataobject. In certain mobile operating systems or application environments,applications may request granular permissions to access privilegedfunctionality on a device, such as sending or receiving network data,accessing the phone's location, reading or writing contact entries, andSMS messaging. In an embodiment, server 151 uses data about permissionsrequested by a data object to determine the capabilities of the dataobject. Server may determine permission data by a variety of means,including metadata and behavioral data reported by devices, marketplacedata, static analysis of data objects, and dynamic analysis of dataobjects. For example, applications on the Android operating system haveto declare permissions at install time, so server 151 may analyze thesedeclared permissions in an application package directly via metadataabout an application package reported by one or more devices or viamarketplace data to determine permission data.

In an embodiment, server 151 performs analysis of a data object'scontent to determine what APIs on a device the data object utilizes. Inan embodiment, the API analysis may include a search of the data objectfor data sequences indicating API calls; an analysis of specificlibrary, function, class, or other import data structures in the dataobject; an analysis of dynamic linker calls; an analysis of calls tolocal or remote services; static analysis of the data object; dynamicanalysis of the data object; and analysis of behavioral data reported byone or more devices. In an embodiment, server 151 utilizes extracted APIcall information to determine that the application has a particularcapability. For example, if an application calls an API to interact witha GPS radio on a device, server 151 determines that the application hasthe capability to determine the device's location. Although suchanalysis may detect the vast majority of APIs used by a data object, itis possible that advanced self-modifying code may prevent thoroughanalysis of a data object. In an embodiment, server 151 detects if thecode is, or may possibly be, self-modifying. The capability of a dataobject to modify itself may signify that the data object is of higherrisk than data objects that are more straightforward. While manyinstances of malware on PCs use self-modifying code to hide fromanti-malware systems, copy-protection systems also often encrypt code toprevent unauthorized access; thus, self-modification alone may not besufficient to classify a data object as malicious, it may be used by ananalysis system, in addition to other characteristics, such asbehavioral data, to produce an assessment for the data object.

In an embodiment, server 151 analyzes behavioral data to determinecapabilities for a data object. For example, server 151 may look for adata object making phone calls, sending SMS messages, accessing theinternet, or performing other actions that indicate a particularapplication capability. In some cases, it is important not only tounderstand what single functions are utilized by a data object, but alsowhether an application exchanges data between APIs. For example, anapplication that uses the internet and can read a device's contact listmay have multiple capabilities that have significantly different risks.For example, an address book application that simply uses the internetto check for updates has less of a privacy risk than an address bookapplication that reads contacts and sends those contacts to theInternet. In an embodiment, server 151 analyzes data object to determineif there are code paths by which data returned or produced by one API orservice are sent to another API or service. For example, server 151 mayperform taint tracking between two APIs to determine if whether anapplication transfers data between APIs. For example, server 151 maydetermine if there is a code path in a data object by which datareturned by any call to the contact API on a mobile device can beprovided to any network API on the device. If there is such a code path,server 151 determines that the data object has the capability of sendingcontacts to the internet. Having such a capability may be more valuableduring further analysis by server 151 or by a user than simply knowingthat an application accesses contacts and that it accesses the internet.Many applications may use both permissions; however, fewer may actuallysend contact data to the internet. A user or an automated analysissystem will be able to use the capability of knowing that there is acode path between two APIs as a much stronger indicator of capabilitiesthan less granular capability measurements.

In an embodiment, server 151 runs a data object in a virtual (e.g.,simulated or emulated) or physical device and analyzes the behavior ofthe data object when run. In an embodiment, the virtual or physicaldevice is instrumented so that it reports behavioral data for the dataobject. In an embodiment, the virtual or physical device's networktraffic, calls, and SMS messages are analyzed by server 151. Forexample, a virtual device may be configured to always report a specificlocation via its location APIs that are unlikely to occur in any realworld circumstance. By analyzing the device's network traffic forvarious encodings of that location, such as a binary double encoding,base 64 encoding, and text encoding, server 151 is able to determinewhether the data object attempts to report the device's location to aserver. In an embodiment, server 151 examines the difference in state ofthe virtual or physical device before the data object is run on thedevice and after the data object has run. For example, a data object mayexploit the kernel on a device upon which it is installed in order toinstall a stealth rootkit. In this case, a virtual device may show asubstantial difference in certain sections of memory, such as in asystem call dispatch table, that should not change under ordinarycircumstances. In an embodiment, the physical or virtual device has acustom root certificate authority in its list of trusted certificatesand server 151 intercepts all TLS traffic, using a server certificatethat is signed by the custom certificate authority, and proxies thetraffic to its original destination. Because the device has a customcertificate authority, the data object is able to establish a valid TLSconnection through server 151 and all encrypted traffic is able to beanalyzed by server 151.

Aside from capabilities of a data object, it may be important for server151 to gather metrics relating to a data object's effect of running on adevice or its usage of capabilities on a device. For example, overuse ofnetwork data, email, or SMS messaging may be considered abusive orindicative of a malicious or exploited application. In an embodiment,server 151 analyzes application data from many mobile communicationdevices, such as metadata and behavioral data, device data, and otherdata it has available to it to produce metric data that characterizes adata object. For example, server 151 may determine how much batteryusage an application requires on average for all devices or for aparticular device type, how much data a data object sends over anynetwork interface or over cellular vs. Wi-Fi network interfaces, howmany email messages or SMS messages a data object sends, how manytelephone calls an object makes, and other metrics.

Server 151 may produce other characterization information from what hasbeen described above that may aid in further analysis by server 151 toproduce an assessment or that may be exposed directly by server 151. Inan embodiment, server 151 analyzes network traffic informationassociated with a data object to produce network characterization data,such as a list of the servers the data object has connected to, theports and protocols on those servers data object communicates with, howmuch data is transmitted to and received from each server. In anembodiment, network characterization information includes whatproportion of devices running a particular data object connect to eachserver. For example, an application that connects to an IM server or aknown malicious bot command and control server may connect to only oneor a small number of servers on all devices that it is installed on;however, a web browser or application that allows user-specifiedconnections may connect to a very large number of different servers ondifferent devices. In an embodiment, if a data object connects to manydifferent servers, server 151 informs one or more devices to not collectnetwork behavioral data for that data object to minimize unnecessarydata reporting. In an embodiment, the network traffic information isgathered as behavioral data from mobile communication devices orgathered by server 151 running the data object on a virtual or physicaldevice.

In an embodiment, server 151 determines whether a data object causes amobile communication device 101 to access malicious Internet or otherpublic or private networks. For example, a data object that causes amobile communication device to access a malicious website may subjectthe device to exploitation. An embodiment of this disclosure allows forresolution of transmitted Inter- or Intranet addresses (e.g., URLs) todetermine whether the address will direct the mobile communicationdevice to a safe website, rather than a nefarious website or phishingscam. This information can be stored as it relates to a particular dataobject.

In order for a user to apply application policy to a mobile devicewithout having to make a separate decision for every single application,it may be helpful to categorize applications so that the user may simplydecide which categories of applications to allow or deny. In anembodiment, server 151 categorizes a data object using data it hasavailable such as application data, device data, marketplace data, andcharacterization data. For example, if a data object is characterized ascalling location APIs on a mobile communication device, then server 151may categorize the data object as a mapping or other location-basedapplication. In an embodiment, categories may directly map tocapabilities, such as applications that read your contact list orapplications that can send your location to the internet. Other examplecategories include whether a data object transmits any information froma mobile communication device's contact list, whether a data objectcauses other data such as a device's phone number to be transmitted by amobile communication device, and other behaviors that may affect theprivacy security of a mobile communication device. In an embodiment,server 151 uses metric data for a data object to categorize it. Forexample, server may have a category of heavy battery users that includesdata objects that typically use more than 10% of a device's battery.Because the categorization may be dependent on device data in additionto characterization data, the category of battery wasters may depend onwhat type of device an assessment is for. For example, a data objectthat uses more than 10% of one device's battery may use only 5% ofanother device's battery.

In an embodiment, if a data object does not directly providecategorization information, server 151 can deduce such information. Forexample, if a data object communicates with a known instant messagingserver, server 151 may determine that the data object is an IMapplication. For example, applications that connect to servers belongingto a popular social network may be classified during analysis as socialnetworking applications, applications that connect to a known maliciousIRC server may be classified as a malicious bot, and applications thatdrain one or more devices' batteries may be flagged as battery drainers.

Because the categorization of an application may be subjective anddifficult to determine automatically, it may be desirable to have one ormore persons, internal to an organization or as part of a collaborativecommunity effort, determine categories for an application. In anembodiment, server 151 exposes an interface by which users can suggestcategories for a data object. For example, server 151 may define acategory of applications that are inappropriate for children, theapplications having content that includes pornography or violence. Inthis example, one or more users can sign in to a community voting systemprovided as a web application where they can search and browse allapplications known to server 151. The list of applications may bepopulated by marketplace crawling and application data reported bydevices. Each application may have a page whereby users can select theirrecommended category for that application. In an embodiment, the userinterface shows information about the data object, such as aggregatedapplication data, characteristics for the data object, and otherinformation available to server 151 so that users can make a decisionbased on the output of analysis. In an embodiment, the user interfaceallows a user to select from a list of categories, add new categories,and add tags for a data object. In an embodiment, the user interface hasa discussion component so that that people may discuss the appropriatecategorization for a data object. In an embodiment, the category for anapplication is determined by a voting system by which users may selecttheir preferred category for the application, the category selected bythe most users being the authoritative category for the application. Inan embodiment, the user interface is displayed on a mobile communicationdevice, displays a list of data objects installed on the device, andallows a user to suggest categories for those data objects.

In an embodiment, server 151 processes application data and device datato determine distribution data for a data object. Distribution data mayinclude how widely a given application is currently distributed, whatthe growth of the application's distribution has been over the period oftime that the application has been available, what customerdemographics, such as geography, have installed the application, andother functions of the prevalence of an application amongst groups ofmobile communication devices. For example, server 151 may examine howmany mobile communication devices report having installed a data objectat the current time to determine how prevalent that application is. Inan embodiment, server 151 uses distribution data to determinetrustworthiness of a data object or to analyze a data object for risk,as is discussed below. For example, an application that has beeninstalled on many devices for a long period of time without beinguninstalled is likely to be less risky than an application that is brandnew and only installed on a few devices.

Because server 151 may encounter legitimate applications that are indevelopment and therefore are not distributed widely, an embodiment ofthis disclosure is directed to server 151 identifying which applicationsmay be in development, thereby preventing them from being classified asundesirable in an anti-malware or other system. Server 151 may receiveapplication data for a data object indicating that the data object hascharacteristics inherent to applications in development, such asdebugging symbols, debuggable permissions or flags, linkage to debugginglibraries, and other characteristics. Applications in development mayalso be likely to have low distribution or isolated distribution. Ifserver 151 identifies that an application is in development, it maystore an indication of the application being considered in developmentand use the indication to prevent server 151 from assessing theapplication as suspicious or undesirable or to decrease the likelihoodthat the server reaches such assessments. In an embodiment, whendetermining whether a data object should be treated as “in development,”server 151 considers previous data objects encountered by devices thatencountered the data object in question. If the devices frequentlyencounter data objects that are in development, server 151 is morelikely to classify the data object as in development. If the devicesinfrequently encounter data objects in development, server 151 is lesslikely to classify the data object as under development.

In an embodiment, server 151 establishes the reputation or level oftrust for the data object. In an embodiment, the level of trust isdetermined manually or automatically and assigned to a single dataobject, multiple data objects that are part of an application, multipleversions of an application, or for all applications from a givendeveloper on one platform or multiple platforms. In an embodiment, trustdata is stored by server 151 on the server or in data storage 111 so itmay be subsequently used directly or as part of producing an assessment.

In an embodiment, trust is granted via a manual review process for anapplication. For example, if server 151 deems application to be riskybased only on its capabilities (e.g., has access to private data and/orutilizes sensitive APIs), a user viewing the assessment may choose notto download it, even if the application is well regarded. To solve thisproblem, the application may be assigned a trust rating by manualreview. If the review deems the application to be trustworthy, theassessment reports the application as not risky; however, if uponreview, the application is determined to be suspicious, the assessmentmay continue to report the application as risky. Because a reputableapplication may consist of multiple data objects, may be updated withnew data objects, or may have versions for multiple platforms, it may beimportant to allow a trust rating to span multiple data objects,applications, and even platforms so that a manual review does not needto be completed for every version or file that is part of anapplication. Similarly, because many reputable software vendors mayproduce multiple applications that can be assumed to be trustworthy, itmay be desirable to automatically grant a high level of trust to dataobjects identified to originate from those vendors. In an embodiment,server 151 grants a data object a high level of trust if the data objectcan be attributed to a trusted vendor or trusted applications throughdata available to server 151 such as the data object's cryptographicsigner, package name, or marketplace metadata.

In an embodiment, server 151 uses distribution data and application datato establish trust for an application. For example, if a popularapplication, such as Google® Maps, is installed on millions of mobilecommunication devices and there are multiple previous versions of theapplication all having the same cryptographic signer and similardistribution characteristics, subsequent versions of the applicationwith that cryptographic signer would be deemed to have a high level oftrust. If server 151 encounters another application that has the samename as a popular application, such as Google® Maps, is installed ononly a few devices, and uses a different cryptographic signer, server151 may grant the low-distribution application a low level of trust. Ananti-malware system may use such data indicating that a data object haslow trust to automatically assess a data object as undesirable or toflag it for manual review. In an embodiment, trust data for anapplication may take into account associated applications such asapplications determined to be created by the same developer on the sameplatform or on different platforms. For example if a company produces anapplication for one mobile platform that has a large number of users andgood ratings, and the company releases a new application on a differentplatform, the new application may be given a high trust rating based onits association to the first application.

In an embodiment, server 151 analyzes application data to determine if adata object is part of a mobile communication device operating system orpreloaded by a manufacturer or operator. In an embodiment, if server 151determines that a data object is part of a mobile operating system or ispreloaded, it is be granted a high level of trust automatically.

In an embodiment, server 151 analyzes user-generated ratings andcomments for an application, such as those gathered by applicationmarketplace data gathering system 1005. For example, server 151 may useratings and reviews to determine a trust rating for the application. Ifan application has low ratings and negative comments indicating that theapplication “crashes” or is otherwise “bad”, server 151 assigns theapplication a low trust rating based on the reputation indicated in itscomments; however, if an application has consistently high ratings andmany reviews, server 151 assigns the application a high trust rating. Inanother example, server 151 uses ratings and reviews to as a subjectiveindicator of application quality for use in producing assessments forthe application. If an application has a significant number of reviewswith text indicating that the application “drains battery” or “sucksbattery”, server 151 determines that the application has the reputationof having adverse battery effects and produces an assessment of theapplication indicating that.

In an embodiment, server exposes trust data to third-parties via an API.For example, trusted applications may be considered certified bylookout. In an embodiment, the trust level exposed by the API is binary(e.g., trusted, not trusted), fuzzy (e.g., 86% trusted, 11% trusted), orcategorical (e.g., fully trusted, malicious, suspicious, semi-trusted).Mobile application marketplaces may wish to display an indicator of thiscertification on an application download user interface as a signal thatthe application has a good reputation. In this case, server 151 mayexpose an API by which third-parties can supply a data object oridentification information for a data object such as a hash identifier,package name, or cryptographic signer. After receiving a data object orenough information to identify one, server 151 responds with anindication of whether the data object is considered certified or not. Inan embodiment, the response is an image indicating whether server 151considers the data object to be certified or not. In an embodiment, theresponse contains a hyperlink to server 151 whereby a user can verifythat the certification for the application is genuine. In an embodiment,the web page referenced by the hyperlink shows additional informationabout the application, such as why it was considered trusted or not(e.g., through manual review, comments, distribution data), whatpermissions are requested by the application, characteristics andcapabilities the application has, and commentary about the applicationduring manual review.

Using data gathered by server 151 or from an analysis system describedherein, server may produce an assessment (block 1113 of FIG. 11). Afterproducing the assessment, server 151 may store the assessment of thedata object so that it may be retrieved at a later time (block 1115).Server may then transmit the assessment for the data object (block1117). For example, server may publish the assessment on an applicationprovider website, provide the assessment in the form of searchablereports, transmit a notification to a mobile communication device,transmit virus signatures containing the assessment that a given dataobject is known good or known bad, and transmit a response to an APIcall querying for the assessment of the data object. Such informationcan be in the form of readable text, a machine readable format, or mayinclude a “score,” a badge, an icon or other symbolic rating. Oneskilled in the art will appreciate that other situations in which server151 transmits an assessment for the data object are possible withoutdeparting from the scope of this disclosure.

In an embodiment, assessment data includes the output from an analysissystem, such as characterization data, categorization data, trust data,and distribution data. For example, an assessment for a data object mayinclude (solely or in addition to other information) detectedcapabilities for the data object, average battery usage for the dataobject, average number of SMS or email messages sent by the data object,the most common servers the data object connects to, the average amountof network data for the data object, and trust ratings for the dataobject. One will appreciate that the above assessment data may beprovided as an input into to server 151. For example, a network operatoror enterprise may operate a server that produces assessment data andfeeds it data back to a master server. In another example, users maydetermine assessment data and provide it to server 151 via an interfacesuch as a web application. In this case, users may provide subjectivetrust data, risk ratings, a categorization, or other assessment datathat may be used by the server. In an embodiment, server 151 combinesassessment data received from multiple sources to produce an aggregatedassessment. For example, if a malware author attempts to transmit anassessment to server 151 indicating that a malicious application is safein the hopes of causing server 151 to produce a false assessment, theserver may utilize the number of unique sources providing assessmentsand the trustworthiness of those sources to produce the aggregatedassessment. If one hundred assessments are received from different,reliable sources such as network operators and enterprises that indicatethe application to be malicious, but ten thousand assessments from aparticular unverified source indicate the application to be safe, theserver produces an aggregated assessment indicating the application tobe malicious.

In an embodiment, assessment data produced by server 151 includes one ormore ratings for a data object. For example, an assessment for a dataobject may include a rating for the data object's privacy by server 151taking into account whether the application has the capability to sendlocation data, contact data, SMS messages, or files from a device to aserver. In another example, an assessment for a data object may includea rating for the data object's security by server 151 taking intoaccount whether there are any known vulnerabilities for the application,whether the application listens for network connections on any ports,whether it meets secure coding guidelines, what the trust level of theapplication is, and whether there are any anomalies in the application(e.g., stealth code, decrypted code, structural anomalies). In anotherexample, an assessment for a data object may include a rating for thedata object's battery impact, such as estimated number of minutes ofphone battery life reduction, by server 151 taking into account bytaking into account the battery usage data reported by devices. Inanother example, an assessment for a data object may include a ratingfor the data object's performance that is produced by server 151 takinginto account the average CPU usage of the application and the frequencywhich the application does not respond to user input events. In anotherexample, an assessment for a data object includes a quality rating thatis produced by server 151 taking into account the frequency ofapplication crashes, user comments, user ratings, and the average timethe application is kept on devices. In an embodiment, server 151provides multiple ratings as part of one assessment so as to provideinformation about a data object along multiple dimensions. In anembodiment, assessments may be binary (e.g., good, bad) or fuzzy (e.g.,100%, 90%, 10%). In an embodiment, multiple ratings are combined into anoverall rating.

In an embodiment, server 151 processes multiple data sources availableto server 151 to produce a rating for the data object. For example,server 151 may utilize application data, device data, characterizationdata, trust data, distribution data, and user-supplied data to determineif an application is malicious. The server may utilize a variety ofsystems or models applied to the data available at the server to producethe assessment. For example, producing an assessment of whether a dataobject is malicious may involve a malware detection system that includesa heuristic engine that analyzes characteristic data to identifybehavior of data objects that are likely to be malicious. Some exampleheuristics include detecting whether a data object utilizes anycapabilities to evade detection by hiding from application enumerationsystems on an the OS it is installed on, whether an application attemptsto modify itself, whether an application has capabilities associatedwith known spyware, and whether an application connects to knownmalicious servers.

One skilled in the art may appreciate that part of the analysisperformed at server 151 to produce an assessment may be seen asextracting features for a data object, and another portion of analysismay be seen as applying a model to those features to produce a usefulassessment; thus, one may apply a variety of systems, such as artificialintelligence systems or algorithms, to process the features for a dataobject to reach a desired form of rating or assessment.

In an embodiment, server 151 produces multiple assessments for a dataobject that take into account different device data or configurationinformation. For example, if server 151 is configured to produceassessments of whether a data object will function correctly and if adata object malfunctions when installed on one type of device, butfunctions correctly when installed on another device type, server mayproduce two assessments for the data object. If server 151 has an API bywhich a mobile communication device 101 can request an assessment for adata object given identifying information for the data object and themobile communication device has sent device data to server 151, thenserver 151 can provide the assessment for the data object thatcorresponds to the device requesting the assessment. If a device 101where the data object would malfunction requests an assessment, thenserver 151 will return the assessment indicating the malfunctioningbehavior of the data object on that device 101. If a device 101 wherethe data object would function correctly requests an assessment, thenserver 151 will return the assessment indicating the correctlyfunctioning behavior on that device 101.

In an embodiment, an assessment indicates whether a data object isallowed to run on a device given policy set by an administrator. Ifmultiple policies are configured on server 151 and data storage 111stores which policy is to be applied to a device 101, then a given dataobject may have multiple assessments that depend on the policy of thedevice querying for an assessment. For example, if a device with astrict privacy policy requests an assessment for an application that canshare a user's location, server 151 transmits an assessment indicatingthat the application is disallowed. If a device with a lenient privacypolicy requests an assessment for the same application, server 151transmits an assessment indicating that the application is allowed. Inan embodiment, assessment data is not stored and only information usedto produce the assessment such as application data, device data,distribution information, characterization information, trust data, andcategorization information is stored and the assessment is performedupon request by applying policy to the stored information.

Although automated analysis systems may produce acceptable results mostof the time, there may be situations in which manual analysis overridesthe result of automatic analysis. In an embodiment, server 151 storesmanual analysis results for a data object and transmits the manualanalysis results as an assessment. For example, server 151 maycategorize an application as a social networking application based onits behavioral data; however, the application may actually be a wordprocessing application that allows the user to publish notes to a socialnetwork. In this case, a user or administrator may override thecategorization for the data object, server 151 storing thecategorization and transmitting it in response to a request for anassessment for the data object. In another example, an anti-malwaresystem identifies data objects having certain characteristics asundesirable. It may also be desirable for a user to manually configureserver 151 to treat particular data objects as undesirable. Server 151stores a list of data objects that are considered undesirable and, whenasked for an assessment for one of these data objects returns anassessment indicating that the data object is undesirable.

Because it may be desirable for assessments about a data object toreflect the most up-to-date information available, in an embodiment,server 151 first produces an assessment and then updates it ifadditional application data or device data becomes available or if theanalysis system itself is updated. In an embodiment, if a data object isre-assessed (e.g., because of new application data, device data, orupdated analysis systems), server 151 stores the new assessment 1111 andtransmits it 1113. For example, after gathering device data andapplication data for a data object from ten devices, server 151 maygenerate an assessment for that data object. Then, if server 151receives device data and application data from one thousand moredevices, it may re-analyze the data object in light of the new data,producing a new assessment for the data object. If the updatedassessment is materially different from the first, actions such asnotifying devices or users may be performed by server 151.

D. Anti-Malware System

In an embodiment, server 151 and mobile communication device 101 areconfigured to function together to prevent malware or spyware fromadversely affecting mobile communication devices. Because mobilecommunication devices are limited in memory, processing ability, andbattery capacity, it may be desirable for server 151 to performanalysis, such as the analysis described herein, to determine if anapplication is considered to be malware or spyware rather than eachdevice performing the analysis. Furthermore, it may be desirable forserver to store the results of the analysis so that if multiple devicesencounter the same application, the analysis does not need to berepeated. Additionally, it may be desirable for server 151 to collectdata about potentially malicious applications, using data collectionsystems described herein, in order to provide data from a variety ofsources for use by analysis systems.

In an embodiment, when mobile communication device 101 assesses a dataobject, such as an application package or executable, to determinewhether the data object is malicious or otherwise undesirable, thedevice sends a request to server 151 for an assessment of the dataobject, the request containing identifying information for the dataobject. In an embodiment, the request transmitted by mobilecommunication device 101 contains application data for the data objectfor use by the server in performing the assessment. For example, inaddition to transmitting identifying information such as anapplication's package name and hash, mobile communication device mayadditionally transmit the permissions requested by the data object andinformation, such as a list of APIs utilized, determined by the deviceby performing static analysis.

In an embodiment, mobile communication device 101 gathers metadata for adata object by using operating system provided facilities andpotentially additional processing. For example, both the Blackberry andAndroid platforms provide mechanisms by which an anti-malwareapplication can query the list of packages installed on a device. Eachalso provides methods to query additional information about the packagessuch as cryptographic signature information and information about howthe packages choose to integrate or expose themselves to the operatingsystem.

In another example, mobile communication device 101 may extract featuresfrom a data object to assist in server 151 producing an assessment. Inan embodiment mobile communication device 101 performs static analysison the data object to extract application data to transmit to server151. For example, on Android, the device may analyze the executableportion of an application packages, typically called “classes.dex”. Thedevice may extract a list of inter-process communication calls directlyor indirectly performed by the executable file that utilize the “binder”mechanism and transmit information about the calls to server 151 for usein analyzing the application package.

In an embodiment, server 151 may analyze the data object immediately, ormay need to gather additional information using a process such as onedisclosed herein. After producing an assessment for the data object, theserver transmits the assessment to mobile communication device 101. Inan embodiment, the assessment contains an indication of whether the dataobject is considered undesirable or not. For example, server 151 maytransmit one of three assessments, known good, known bad, and unknown.If the server determines that the data object is known to be good (e.g.,because it has a high trust level), it will return an assessment thatthe data object is known good. If the server determines that the dataobject is known to be bad (e.g., because it is determined to be a pieceof malware), it will return an assessment that the data object is knownbad. If the server does not have enough information to make adetermination, it will return an assessment that the data object isunknown. In an embodiment, the assessment contains a risk level of thedata object or a confidence level of the known good or known badassessment so that mobile communication device or its user can use therisk or confidence level to determine how to classify the data object.

In an embodiment, the assessment transmitted by server 151 to mobilecommunication device 101 contains information as to why server 151determined that the data object was undesirable. For example, server 151may transmit the name of a malware family the data object was determinedto belong to or server may transmit an HTTP URL referencing server 151that mobile communication device 101 can use to display additionalinformation about the data object, the URL containing an identifier thatis decoded by server 151 to allow it to retrieve stored informationabout the data object. The web page may display additional informationsuch as the output from different analysis systems used to produce theassessment. For example, the web page may display distributioninformation for the data object, information about common serversconnected to by the data object, information provided by human analysisof the data object, trust data associated with the data object,information about the geographic distribution of the data object,information about similar data objects, and information about the authorof the data object.

It may be desirable to minimize requests mobile communication device 101needs to send to server 151 for assessments of data objects so that thedevice minimizes the amount of data it transmits and receives, reducestime required to assess a data object, optimizes battery consumption,and minimizes load on server 151. In an embodiment, a mobilecommunication device 101 maintains a local cache of assessmentinformation received from server 151. The local cache may be storedusing a lightweight database such as SQLite or in a proprietary binaryfile format that is optimized for assessment storage. For example, thecache may contain an indication as to whether a data object wasundesirable or not, a risk level associated with a data object, anddefinition information such as identifying information for a dataobject. When a device scans a data object, it can look up the dataobject's identifying information in the local cache. If an assessmentfor the data object is cached, that assessment is used. If an assessmentis not cached, the device retrieves an assessment from server 151. In anembodiment, when a mobile communication device inserts an assessmentinto its cache for a data object encountered on the device, it generatesdefinition information for the data object. For example, a device mayuse the hash of a data object's content to ensure that it cachesassessment results from a server. In an embodiment, server 151 transmitsdefinition information with an assessment so that mobile communicationdevice can apply the assessment to the appropriate set of applications.For example, in some cases server 151 may indicate that an assessmentonly applies to a specific data object identified by a hash of itscontents while in other cases the server may indicate that an assessmentapplies to all data objects signed with the same cryptographic key.

In an embodiment, a mobile communication device 101 stores a local cacheof definitions for known good data objects and known bad data objectsfor use by a recognition component (described below) operating on themobile communication device. Using the recognition component, the mobilecommunication device can determine an assessment for a suspect dataobject if the local cache contains a definition and correspondingassessment that corresponds to the suspect data object. For example, thedefinitions may use criteria such as hash identifiers, package names,and cryptographic signers to match a data object. Each definition mayhave a corresponding assessment (e.g., “good”, “bad”). If a definitionmatches a suspect data object, the definition's assessment is used forthe suspect data object. If no definitions correspond to the dataobject, such as the data being recognized as safe or not safe, then themobile communication device 101 may transmit application data for thesuspect data object to server 151 for more comprehensive analysis.

In an embodiment, the cache is used as the primary storage ofanti-malware definitions that determine whether anti-malware software onmobile communication device 101 will recognize a data object asmalicious or not without having to consult server 151. In an embodiment,the cache stores definition information used by a recognition componenton the device. For example, the cache may contain definition informationsuch as package names, cryptographic signers, byte sequences, patterns,or logic that is used to match data objects on a device with cachedassessments. If the cache contains an entry linking a particular bytesequence to an assessment of being a malicious application and a dataobject on a device contains that byte sequence, then the device willdetermine that data object to be malicious without having to contactserver 151. In an embodiment, the cache only contains definitioninformation, all definitions corresponding to a single assessment of adata object being malicious. In an embodiment, the cache may containassessment information, the assessment information possibly containingan identifier, as discussed above, which can be transmitted to server151 in order for the device to retrieve information for display to auser. Such an identifier being used to retrieve data from server 151allows the cache to minimize the information it stores about potentialmalware. In an embodiment, a device cache serves as both a whitelist anda blacklist. The cache contains definition information for known goodand known bad data objects so that if a data object is determined to beknown good or known bad, the device does not need to request anassessment from server 151. In an embodiment, the cache that serves asboth a blacklist and a whitelist is used by a recognition component onthe mobile communication device to determine if data objects arerecognizably bad or recognizably good. If a data object encountered by adevice is neither recognizably good nor recognizably bad based ondefinition data stored in the cache, then the device may transmitapplication data for the data object to server 151 so the device canreceive an assessment for the data object from the server. In anembodiment, anti-malware software on a mobile communication device isinstalled with a pre-populated cache of definitions that are modified bythe device as it receives new assessments or stored assessments aredeemed to be invalid.

In an embodiment, assessments and definitions cached on a device areonly considered valid for a period of time so that the mobilecommunication device does not rely on data that is potentially out ofdate. In an embodiment, cached assessments and definitions are storedindefinitely and considered to be valid without time constraint. In anembodiment, a device only stores certain types of assessments anddefinitions. For example, a device may only cache known good assessmentsor may only cache known bad assessments. In this case, definitions areonly stored if they have a corresponding assessment. In an embodiment,part of the cache is stored in volatile storage, such as RAM, and partof the cache is stored on non-volatile memory, such as flash. Becausevolatile memory is typically more limited yet much faster thannon-volatile memory, a device may store frequently accessed assessmentsand definitions in volatile memory while less frequently accessedassessments and definitions in non-volatile memory. For example, if ananti-malware system analyzes data objects every time they are opened, itmay be desirable to very quickly determine an assessment for a dataobject if it has been recently scanned and not changed. By storing arecently used definition and assessment in volatile memory, the devicecan recall the previous assessment very quickly.

In an embodiment, server 151 transmits cache control information with anassessment, indicating whether the device should cache it and, if so,for how long. For example, server 151 may transmit an assessment for apopular application from a reputable company, including cache controlinformation indicating that a device should cache the assessment. Ifserver 151 transmits an assessment for a lesser-known application, itmay include cache control information indicating that a device shouldnot cache the assessment, as the application may turn out to beconsidered undesirable in the future after more is known about it. In anembodiment, server 151 determines cache control information based on theconfidence of an assessment. For example, known good assessments forapplications that have a high trust level may be considered to be highlyconfident while assessments indicating that an application is unknowndue to lack of data available to the server may not be consideredconfident. In an embodiment, when an assessment expires, cacheddefinition information associated with the assessment is also expired.

Because retrieving cached assessments is faster than retrievingassessments from server 151 (thereby minimizing the delay and overheadwith determining whether a data object is malicious or not), it may bedesirable to maximize the number of assessments that can be determinedlocally from cached data. In an embodiment, server transmits assessmentsto a mobile communication device without the device requesting theassessments and the mobile communication stores these assessments in itscache. Because all of the assessments available to server 151 mayrequire more storage than is desirable on mobile communication device101, server may only transmit a subset of its available assessments. Inan embodiment, server 151 determines which assessments to transmit tomobile communication device 101 by analyzing device data and applicationdata. For example, server 151 may store the operating system a dataobject is compatible with associated with assessments for data objectsin such a way that the server can query for all of the assessmentsrelated to a given operating system. Server 151 may then only transmitassessments to a mobile communication device that are for data objectsthat are compatible with the operating system the device is running. Theother assessments would not be transmitted to the device because thedata objects referenced by the other assessments are not able to run onthe device's operating system. In another example, server may use adevice's country, language, or area code to determine what assessmentsto transmit to the device. Users in the United States are unlikely todownload Russian-language applications, just as users in Russia areunlikely to download Spanish-language applications.

In an embodiment, server 151 stores which assessments it has alreadytransmitted to a device and the device has successfully received so thatassessments are not unnecessarily re-transmitted. If a device has notreceived assessments that are desired, the server transmits theassessments the next time the device connects. In order to efficientlytrack which assessments have already been received by a device, server151 may group assessments, such that a given device receives allassessments in one or more groups. For example, a given group ofassessments may have changes (e.g., new data objects being assessed,changes to existing assessments) multiple times per day; however, adevice may be configured to receive updated assessments only once perday. To determine what assessments to transmit to a device, server mayrecord the time when a device has last received up to date assessmentsfor a group and only examine changes to the group since the device haslast received assessments. For example, if a device receives all of theassessments for a given group on Monday and two new assessments areadded to the group on Tuesday, then, if the device connects onWednesday, the server only needs to query what assessments have changedin the group since Monday and will determine that it needs to transmitjust the two added assessments. In an embodiment, server utilizes a pushservice such as one described herein to alert a device that there areadditional assessments that server is ready to transmit to the device.When using such a push service, when server updates assessments that arepart of a group, all devices that receive assessments from that groupcan be updated with the latest assessments nearly immediately.

There are a variety of ways in which assessments can be grouped byserver 151 in order to selectively transmit assessments to a device. Forexample, there may be more assessments for data objects compatible witha given operating system than it is desirable to store on a device. Inthis case, the server may produce a group of assessments that correspondto the most prevalent data objects, based on distribution data or marketdata available to server 151. In this case, devices will cacheassessments for the data objects they are most likely to encounter. Itis also possible to further improve the likelihood that a device hasassessments cached for data objects it encounters by server 151analyzing the application data available at the server corresponding tothe data objects previously encountered by the device and predicting,based on those previous encounters, what data objects the device islikely to encounter in the future. Assessments for these likely dataobjects can then be transmitted to the device.

Because the optimal amount of assessment data to cache on a device maybe different depending on a device's hardware, user behavior, or userpreferences, it may be desirable for that amount of data to be tunable.In an embodiment, the amount of assessment data to cache on a mobilecommunication device 101 is determined by server 151. For example,server 151 may examine the amount of storage available on a device, thefrequency by which a user downloads applications, and how likelyadditional cached assessment data will be to reduce the number ofrequired assessment requests transmitted by the device. If a device hasa lot of available storage and its user downloads a lot of applications,then the server may determine to cache a large amount of assessmentdata; however, if a device has little available storage and its userrarely downloads applications, then the server may determine to cacheonly a small amount of data or no data. The server may also examineprevious assessment requests made by the device to determine if thoserequests could have been avoided by the device caching additionalassessment information. For example, if a device currently receivesassessments belonging to a particular group of applications and theserver is evaluating whether device should receive assessments for anadditional group of applications, the server examines previouslyassessment requests to determine how many of those assessments were inthe second group. If server 151 determines that enough of theassessments requests would have been avoided, then it will starttransmitting assessments from both groups to the device. In anembodiment, a user can control the amount of storage to allocate tocached assessments on a mobile communication device 101.

Instead of always producing an absolute assessment (e.g., known good orknown bad), it may be desirable for server 151 to report that it doesnot yet have an assessment. In an embodiment, server 151 transmits anassessment for a data object indicating that the object's undesirabilityis unknown. When mobile communication device 101 encounters a dataobject, it transmits a request to server 151 for an assessment, andreceives an unknown assessment, the device temporarily trusts the dataobject and retries the request for assessment at a later time. In orderto avoid unnecessary requests, the device increases the time delaybetween retries if it continues to receive unknown assessments. Duringsuch a period of temporary trust, the device does not re-transmit anassessment request every time a data object is scanned. For example, inan anti-malware system on a mobile device designed to scan files on afile system when they are accessed, the first access to a data objectmay result in the device transmitting an assessment request to server151. If the server returns an unknown assessment, then the device storesa temporary entry in its assessment database indicating identifyinginformation for the data object, a temporary assessment indicating thatthe data object is allowed, and the time period the assessment is validfor.

In an embodiment, server 151 transmits information about a data objectin an unknown assessment and mobile communication device 101 uses thedata assessment from server 151 as an input into a local analysissystem. For example, mobile communication device 101 may have aheuristic system that analyzes the content of a data object to determineif it is malicious. In the case of a known good or known bad result fromserver 151, then the device either does not run the heuristic system ordiscards the result from the heuristic system. If server 151 returns anunknown result including a trust level for the data object, device 101combines result from the heuristic system with the trust level providedby the server to determine whether to treat the data object asundesirable or not. For example, mobile communication device 101 mayscale the result from local analysis based on the trust level reportedby server 151. If a heuristic system on the device determines that adata object is 66% risky and an unknown assessment from server 151indicates that the data object has a suspicious 1% trust level, thedevice determines that the data object is undesirable; however, if theunknown assessment from server 151 indicates that the data object has a70% trust level, then device 101 determines that the data object isdesirable.

In order to respond to undesirable applications, such as malware andspyware, as soon as they are identified as such, it may be desirable forserver 151 to transmit notifications to mobile communication device 101about data objects that are determined to be undesirable afterpreviously being classified as good or unknown. In an embodiment, server151 stores information about data objects encountered by mobilecommunication device 101 so that if a data object encountered by thedevice was assessed to be good or unknown but was subsequentlydetermined to be undesirable, server 151 may determine all of thedevices that have encountered the data object and transmits anotification indicating that the data object is undesirable. In anembodiment, server 151 only transmits a notification to device 101 ifthe data object that is the subject of the notification can operate onthe device's operating system. For example, if a device runs Blackberryand has encountered an Android spyware application, server 151 would nottransmit a notification to the device; however, if the deviceencountered a Blackberry spyware application, server 151 would transmita notification. As disclosed herein, the determination of whether a dataobject can operate on a given device may be determined by analyzingdevice data for the device and application data for the data object.

In an embodiment, the notification transmitted from server 151 to device101 is designed to be consumed by the device and includes bothidentification information and remediation information for the dataobject. For example the notification may utilize a push service providedby a platform vendor and include the package name and content hash for adata object. The notification may also specify a remediation action suchas “killing” any processes containing the data object, requesting for auser to uninstall the data object, and deleting the data object withoutuser intervention. In an embodiment, the notification includesinformation for display to a user about the data object such asremediation instructions, an explanation for why the data object isconsidered undesirable, or a request to take a particular action. In anembodiment, the notification is in the form of a human readable message,such as a text message, email, or telephone call. It may be desirablefor server to perform both human readable and machine readablenotification to ensure that a user responds to a dangerous data object.For example, server may transmit an email message to a user and transmita notification for the device to remove the data object without userintervention.

In an embodiment, mobile communication device 101 contains a database ofall data objects that are present on the device and server 151 transmitsupdated signature data to the device when a data object encountered bythe device is determined to be undesirable. When the device receives theupdated signature data, it compares the updated signature data to dataobjects present on the device. If any objects that are present on thedevice are considered by the updated signature data to be undesirable,then the device immediately initiates remediation actions, not waitingfor the next time the data object is scanned.

If an anti-malware system performs an assessment for a data object, itmay be desirable to trust the data object as long as it hasn't changedto avoid having to re-assess the data object. In an embodiment, mobilecommunication device 101 maintains a list of data objects identifiedthat have been analyzed and are considered to be desirable. When a dataobject is desired to be scanned, the device may first check this list tosee if the data object is present. If the object is present, the devicedoes not re-scan the object. After scanning a file and determining it tobe desirable, the device places an identifier for the data object in thelist. Example identifiers include a file name, filesystem nodeidentifier, or operating system specific data object handle. In anembodiment, the mobile communication saves this list of data objects tonon-volatile storage so that the list can be preserved even if thedevice is rebooted or runs out of battery. When storing assessments andlater accessing them, it's important that any stored assessments arevalid only for a particular set of data object content. If the dataobject's content changes, a different assessment may be necessary, asthe data object may have been modified to include malicious code thatwas not present in the original data object. In an embodiment, the listcontains a cryptographic hash of the content of the data object. Whenthe device determines whether the data object is considered to be on thelist, it compares the hash of the data object as stored on the devicewith the hash stored in the list. If the hash matches, the data objectis considered to be on the list. In an embodiment, the anti-malwaresoftware can determine when files are opened and closed. If a file onthe list is opened with write access, then it is removed from the list.While there are open writers to the file, the file cannot be added tothe list.

One will appreciate that an embodiment of this disclosure contemplateother ways for reducing network traffic while providing sufficientoptions for securing mobile communication devices. In an example, amobile communication device can request an analysis of all of the dataresident on the device (a “scan”) when the mobile communication devicefirst starts up or powers on, or when the application responsible formonitoring the mobile communication is first launched. This provides abaseline analysis of the security of the mobile communication device.Future scans may be performed when new applications are accessed by themobile communication device, or at pre-set time intervals, or upon userrequest. Scans may be adjusted depending upon the access to network 121.If connectivity is an issue, then only newer data may be assessed, orsuspect data. Scans may be queued and performed when connectivityimproves.

In an embodiment, an anti-malware system on mobile communication device101 has the capability to perform both an on-demand and a scheduled scanof all data objects present on a device. If the anti-malware systemutilizes server 151 to perform assessments for the data objects, it maybe desirable to optimize the time required to perform the scan. Becausenetwork latency causes a delay between the time a request for anassessment is transmitted by a device and the time the device receives aresponse from server 151, it may be desirable to pipeline requests insuch a way that the device does not simply idle while waiting for aresponse. In an embodiment, mobile communication device transmits arequest to server 151 to provide assessments for multiple data objectsand server 151 transmits assessments for those multiple data objects tothe device. For example, during an on-demand scan, a device may beconfigured to first enumerate all of the data objects on the device andthen send a request to server 151 to assess all of the enumerated dataobjects. In another example, a device may enumerate ten data objects ata time, then send a request to the server and receive a response forthose ten data objects before scanning additional data objects. Inanother example, a device may enumerate data objects and transmitassessment request, continuing the enumeration process without waitingfor assessment responses from the server. The device may only wait forresponses when the enumeration is complete.

In an anti-malware system that blocks the loading or executing of a dataobject until the system has reached a disposition, it may be desirableto assess a data object before it needs to be loaded or executed. In anembodiment, mobile communication device 101 proactively scans dataobjects and stores the results so that when the data object is loaded,the device can reference the previous scan result. For example, when adevice loads a program that depends on multiple other files (e.g., anexecutable that is linked to shared libraries), an anti-malware systemon the device may analyze the program to determine all of the librariesit depends on, send a request to server 151 for assessments for theprogram and its dependent libraries, and then allow the program'sexecution to proceed once the device receives positive assessmentresults. When the device's operating system loads the libraries theapplication depends on, no request to server 151 is needed because thesystem already has up-to-date assessments for the libraries. If thelibraries were not proactively analyzed, the total load time for theprogram could be greater as the device may have to wait for multiplerequests to server 151 to occur in serial. In an embodiment, software ona mobile communication device analyzes data objects after they aredownloaded but before they are executed. For example, anti-malwaresoftware on a device may watch the download directory for new files ormay simply wait for files to be created, written to, and then closed.After the download completes, the software may initiate a scan of thenew file so that once the file is opened, the system already hasassessed it and can recall the previous assessment.

If an anti-malware system blocks user-requested or system operationswhile it is assessing a data object, it may be desirable to give theuser an indication that an assessment is in progress, especially if theassessment depends on a network connection that may have significantlatency. In an embodiment, an anti-malware system on mobilecommunication device 101 displays a user interface indicating that adata object is being scanned when the system is scanning the data objectand blocking user-requested operations. For example, if an anti-malwaresystem prevents the execution of applications until the application andall of its dependent libraries have been assessed by interposing itselfin the application launch process, there can be a significant delayperceivable to the device's user. The annoyance associated with thedelay may be mitigated by informing the user what is happening insteadof the device simply seeming unresponsive. When a user launches anapplication, the device displays a user interface view indicating thatthe anti-malware system is assessing the application that the userlaunched. In an embodiment, the user interface allows the device's userto skip waiting for the scan to finish. For example, if the device'sscanning of a data object needs to connect to server 151 and the userdoesn't want to wait, the user may proceed without waiting for theassessment to return. If the assessment subsequently returns that thedata object is malicious, the device may initiate remediation actions,such as killing any processes containing the data object and deletingthe data object, even though the data object was allowed to run.

A user may be interested in having an application assessed, but does notwish to wait for a response from server 151. The user may choose toforego complete analysis and use the application while waiting foranalysis results. In such a situation, it would be helpful if server 151or the user's mobile communication device 101 could provide a temporarytrustworthiness evaluation prior to formal analysis. Reporting can be inthe form of an interface element, a notification, a warning, a riskrating, or the like. In an embodiment, the mobile communication device101 can run a local analysis to determine whether an application istemporarily trustworthy. It may also be desirable to show informationabout a data object on a user interface that indicates when ananti-malware system is waiting for an assessment from a server so thatusers do not accidentally skip items that are high risk. In anembodiment, the waiting user interface shows the result of localanalysis while waiting for an assessment from server 151. For example,the user interface may show the capabilities of the data object or arisk score for the data object. In an embodiment, the device only allowsa user to skip waiting for an assessment from server 151 if localanalysis determines that the data object is low risk. For example, arisk score may be calculated by analyzing what sensitive functionality adata object accesses. A data object that accesses a user's contact listand browser history may be deemed more risky than a data object thatdoesn't access any sensitive functionality.

In an embodiment, an anti-malware system on device 101 determineswhether it should wait for a response from server 151 before reaching aconclusion based on the context of the scan. For example, scans thatoccur during system startup or when there is no active networkconnection should not block waiting for a response from the server. Inorder to determine if there is a network connection, the anti-malwaresystem may rely on a variety of methods such as querying networkinterface state information provided by the operating system andanalyzing whether requests to server 151 time out. If the anti-malwaresystem intercepts system calls, scans that occur as a result of thesystem trying to execute a data object should block while waiting for aresponse from server 151 while scans that result from an applicationgetting information about a data object (e.g., file manager extractingan icon for the data object) should not block while waiting for aresponse. In an embodiment, if a request for a data object assessment isunable to be completed, it is retried at a later time.

In an embodiment, the anti-malware system skips portions of server orlocal analysis if an accurate assessment can be produced without theadditional analysis. For example, if local analysis determines that adata object is not risky, then the device may not request an assessmentfrom server 151—the device may only request an assessment from server151 if the data object being scanned has a minimum riskiness asdetermined by a local analysis component on the device. In an example,the determination of whether to skip waiting for additional results isdetermined by both the results and which system returned each result. A“bad” result from local analysis before receiving a result from server151 may be enough to treat a data object as malicious; however, a “good”result from local analysis may still require the system to wait for anassessment from server 151 to confirm that the data object is goodbefore determining a final disposition.

In an embodiment, if multiple analysis systems produce differentresults, the anti-malware system on a device analyzes the results of thesystems to make a determination as to the final disposition of a dataobject, the determination taking into account both what results wereproduced and which system produced each result. For example, theanti-malware system may determine that a single undesirable result isenough to flag a data object as undesirable. In another example, server151 may be treated as authoritative or server 151 may transmit aconfidence level of its assessment so that device 101 can determinewhether to treat the assessment as authoritative or not. In anotherexample, known bad results from server 151 may be authoritative butknown good results from server can be overridden by a known bad resultfrom a local analysis system on device 101.

In an embodiment, server 151 stores a list of malware or otherundesirable applications that have been detected on the device and whichare still active on the device. In order for this list to be populated,mobile communication device 101 sends events to server 151, includingwhenever it encounters an undesirable application, whenever anundesirable application is removed, and whenever an undesirableapplication is ignored. The events include identifying information fordata objects so that server 151 can correlate the events with known dataobjects. For example, because a user may choose to ignore malware, it'simportant for the user to be able to see his or her list of ignoredmalware to avoid a situation where a malicious user installs malware onsomeone else's phone and configures anti-malware software on the phoneto ignore the malware, preventing the system from automatically removingit. In this circumstance, the legitimate user of the phone is able totell that a piece of malware is active on his or her device, but isignored. In an embodiment, because server 151 has data indicatingwhether device 101 currently has active malware, network access can beallowed or denied to the device depending on its malware state by anetwork access control system querying server 151 for the state of agiven device.

In an embodiment of this disclosure, server-side or “cloud” analysis maybe performed using a version of the three-component system described inU.S. patent application Ser. No. 12/255,621, which is incorporated infull herein. An example of a three-component system is illustrated inFIG. 9 and includes a first component 903 that may be used to recognizedata that is safe, or “known good” (also referred to herein as formingpart of or being included on a “whitelist”). A second component 905 maybe used to recognize data that is malicious, wastes device resources, oris “known bad” (also referred to herein as forming part of or beingincluded on a “blacklist”). A third component 907 is a decisioncomponent that may be used to evaluate data that is neither known goodnor known bad, i.e., “unknown.” In an embodiment, known good component903 and known bad component 905 may reside on mobile communicationdevice 101, and decision component 907 may reside on server 151. In anembodiment, known good component 903, known bad component 905 anddecision component 907 may all reside on server 151. In an embodiment,portions of known good component 903, known bad component 905 and/ordecision component 907 may reside on mobile communication device 101,and portions of known good component 903, known bad component 905 and/ordecision component 907 may reside on server 151. In an embodiment, knowngood component 903 and known bad component 905 reside on server 151while decision component 907 resides on mobile communication device 101.

For example, data store 111 may contain malware definitions that arecontinuously updated and accessible by server 151. The mobilecommunications device 101 may be configured to send application data,such as a hash identifier, for a suspect data object to server 151 foranalysis. Server 151 may contain known good component 903, known badcomponent 905 and decision component 907, or the components may bedistributed across two or more servers. The one or more servers maythereby use application data to determine if the suspect data object isa recognizably safe data object. If the suspect data object isrecognizably safe, then the one or more servers may notify the mobilecommunications device or instruct the device that it may accept andprocess the data object. The one or more servers may then useapplication data to determine if the suspect data object is recognizablymalicious. If the suspect data object is recognizably malicious, thenthe one or more servers may notify the mobile communications device orinstruct the device to reject the data object and not process itfurther. The known good and known bad components may have a variety ofmethods for recognizing known good and known bad data objects. The data,logic, and any other information used by known good and/or known badcomponents to identify recognizably good or recognizably bad dataobjects, respectively, may be called “signatures” or “definitions”(explained further below).

If the known good and know bad components are inconclusive, one or moreservers may perform additional analysis to reach a decision as to thedisposition of the data object. In an embodiment, server 151 contains adecision component that uses one or more analysis systems to analyzeapplication data for the data object and make a determination as towhether the data object is considered undesirable or not. In anembodiment, if there is not enough information to perform the additionalanalysis, then the one or more servers may request that a mobilecommunications device send additional application data to the server foranalysis. For example, a device may initially send a hash identifier,package name, and cryptographic signer information for a data object toa server for analysis. If the known good or known bad component fails toidentify the data object as known good or known bad, the server mayrequest that the device send the whole data object to the server so thatthe data object itself may be analyzed. Upon receiving additionalapplication data, further analysis to reach a disposition for whether adevice should accept or reject the data object may be performed by adecision component 907 or manually. In an embodiment, the server storeswhether or not a given data object needs manual analysis so that ananalysis team may easily determine what data objects need to beanalyzed.

Because an assessment for a data object may rely on human analysis to beproduces, server 151 may use analysis systems to produce store a list ofsuspicious data objects that need further study. In an embodiment, someresults from analysis systems on server 151 produce assessments that aretransmitted to mobile communication device 101 and others identify dataobjects as needing human analysis. For example, if server 151 utilizes aset of heuristics to identify malicious applications, some set of theheuristics may be well tested and provide acceptable accuracy incorrectly identifying malicious behavior while another set of heuristicsmay be experimental, requiring human analysis to determine if theresults are acceptable.

The following describes each of the components identified above in moredetail. A person skilled in the art will appreciate that since the totalnumber of known good applications for mobile communication devices canbe identified, use of the known good component 903 coupled to adatabase, logic, or other data store containing definitions for knowngood data objects (e.g., application data such as hash identifiers) maysignificantly reduce false-positive undesirable application detectionand reduce the need to perform computationally expensive analysis or tocontact a server for analysis. One will also appreciate that use of aknown good component 903 may be particularly effective for data thatcontains executable software code. Executable software code for a givenapplication rarely changes between different mobile communicationsdevices, so creating a database of known good application data or logicfor evaluating application data may be an effective method forrecognizing safe or trustworthy data. This database may vary in sizedepending upon the resources available on the mobile communicationsdevice. Alternatively, aspects of this disclosure, such as the knowngood component and known bad component, may have access to a remoteserver with a larger library of application data for known good or baddata objects, such as server 151 coupled to a data store 111 in FIG. 1.

In an embodiment of this disclosure, known bad component 905 may haveaccess to a database, logic, or other data store containing definitionsfor known bad data objects that can be stored on the mobilecommunications device without occupying a significant amount of memory.For example, virus and other malware or spyware definitions can includeapplication data such as hash identifiers, package names, cryptographicsigners, byte sequences, and byte patterns stored in a database or othermemory cache. In other words, there may be a known bad database thatcomplements the known good database stored on mobile communicationsdevice 101. Additionally or alternatively, known bad component 905 maybe capable of identifying malware using characteristics common to othermalicious software code. When applied to network data or data files,known bad component 905 may have access to a database containingpatterns or other characteristics of a protocol data unit or file formatwhich presents a security threat. Known bad component 905 may alsoidentify data that undesirably affects a mobile communication device,such as exposing vulnerabilities, draining battery life, transmittingprivate or unauthorized information to third parties, or using upunnecessary device resources. Similar to the known good component 903and database, any data identified as “bad” may be deleted, quarantined,or rejected from further processing by the mobile communications device.If a known bad data object is detected, an embodiment of this disclosuremay also display a notification or other message similar to thatdescribed in U.S. patent application Ser. No. 12/255,635, entitled“SECURITY STATUS AND INFORMATION DISPLAY SYSTEM,” filed on Oct. 21, 2008and incorporated in full herein.

Decision component 907 may be used to evaluate data that cannot becharacterized as either known good or known bad. Since a majority of thedata received on the mobile communications device 101 may fall withinthis category, this component may reside on server 151. This componentmay utilize a variety of methods to produce an assessment for a dataobject, including using any of the analysis systems disclosed herein.For example, decision component 907 may apply static analysis, dynamicanalysis, distribution analysis or other methods of analysis in order todetermine whether received data may be passed to its intendeddestination or rejected to prevent harm from befalling the device.Examples of this analysis are discussed below.

The following examples illustrate how one or more servers can be used toaugment or replace the methods described in U.S. patent application Ser.No. 12/255,621.

Multiple systems containing known good component, known bad component,and decision component are possible. Depending on the specific types ofdata being analyzed and the types of security threats being prevented,different orders of execution and logic applied to each component'soutput can be employed. In an embodiment, if data is not determined tobe good by known good component 903 (block 805), it will be rejectedfrom processing 813. Data that known good component 903 determines to begood (block 805) is still analyzed by known bad component 905 (block807). If known bad component 905 determines data to be bad (block 807),it is rejected from processing 813, otherwise data may be analyzed bydecision component 907 (block 809). In an embodiment, if data is notdetermined to be known good by known good component 903, known badcomponent 905 analyzes it. If known good component determines the datato be good, it is allowed. If known bad component 905 determines thedata to be bad, it will be rejected from processing 813. If known badcomponent 905 does not determine the data to be bad, the data may beanalyzed by decision component 907 to reach an assessment for the data.

An example analysis of network data or data files present on a mobilecommunication device is shown in FIG. 8. As shown in FIG. 8, block 801may involve gathering data sent to or received from the mobilecommunications device. The data may be analyzed to identify its protocoland track state (block 803). In block 805, known good component 903resident on the mobile communication device may evaluate the gathereddata for known good characteristics. Known good characteristics mayinclude the characteristics previously discussed or described in U.S.patent application Ser. No. 12/255,621. If the data contains sufficientknown good characteristics, it may be allowed to proceed to its intendeddestination (block 811) for processing, execution or other operation.Alternatively, the data may be further analyzed by known bad component905 resident on the mobile communication device to confirm that the datais truly safe (block 807). If known bad component determines that thedata is truly safe, then the data may be allowed to proceed to itsintended destination (block 811). Decision component 907 may also beavailable to provide a final check (block 809) before allowing the datato proceed (block 811).

Analysis of a data object may be performed at any time. For example, thedata object may be evaluated prior to access or download, or afterdownload but prior to installation, or after installation, prior toinstallation of a new version of the data object, or after theinstallation of a new version of the data object, if the data is anapplication. In an embodiment, a data object that has not yet beendownloaded to a device is evaluated by using identifying informationabout the data object. For example, if an application market accessibleto a mobile communication device makes applications available fordownload and provides identifying information about the data object suchas a hash of the application's content or a package name for theapplication, software on the mobile communication device can use theidentifying information to determine an assessment for the applicationby evaluating the identifying information locally using any of thesystems described herein or by transmitting the identifying informationto server 151 and receiving an assessment from the server. In thismanner, the software on the mobile communication device can assesswhether applications are undesirable or not before a user downloadsthem.

At any point during the analysis, if either known good component 903,known bad component 905 or decision component 907 (discussed furtherbelow) determines that the data is not good, or affirmatively containssecurity threats, data inconsistencies, etc., then in block 813 the datawill be blocked, rejected, deleted or quarantined. In an embodiment ofthis disclosure, a signal event or security event information log may beupdated to record the encounter with the contaminated data.

The analysis of executable data such as applications, programs and/orlibraries on the mobile communications device may proceed as illustratedin FIG. 9. In block 901, the executable is determined to need to beclassified as either good or bad as a result from an attempt to accessthe executable, installing the executable, or the executable beingdownloaded or otherwise transferred to the mobile device. The executablemay or may not be pre-processed to extract additional application datasuch as a hash identifier, cryptographic signer, package name or othercharacteristics before being evaluated by known good component 903resident on the mobile communication device (block 903). This evaluationmay include comparing the executable's hash identifier or othercharacteristics against a database of known good characteristics,identifying whether the executable has sufficient known goodcharacteristics, or any of the criteria discussed above or described inU.S. patent application Ser. No. 12/255,621.

If the executable is recognized as known good, then in block 911, it maybe allowed to execute its code or proceed to its intended destinationfor processing or other operation. If known good component 903 fails toallow the executable data, then known bad component 905 resident on themobile communication device may perform its analysis (block 905). Ifknown bad component 905 confirms that the executable is malicious, thenthe executable may be quarantined, rejected, or deleted, and the eventmay be logged (block 909). If known bad component 905 is unable tocharacterize the executable, then the decision component 907 may performits analysis as described further below (block 907). If decisioncomponent 907 ultimately determines that the executable is safe, thenthe executable is allowed (block 911). If decision component 907ultimately determines that the executable is not safe, or remainsunsure, then the executable may be quarantined (block 909). One willappreciate that since executables may contain code that can causesignificant harm to the mobile communications device, it may requiremore rigorous analysis before the executable is allowed to proceed.

One will appreciate that known good component 903 and known badcomponent 905 can be kept lightweight on the resident mobilecommunication device by only storing definition information about thoseapplications most likely to be accessed by the mobile communicationdevice. As described above, such information may be determined, forexample, based upon device data, the applications previously installedon the mobile communication device, and the way the mobile communicationdevice is used (e.g., work versus entertainment, accessing publicnetworks versus private networks, etc.). One will appreciate that eachmobile communication device may store different definition information,and that an embodiment of this disclosure contemplates such granularity.

As discussed above and throughout, an embodiment of this disclosure isdirected to server-side analysis of data in the event that known goodcomponent 903 and known bad component 905 are unable to determinewhether the data is safe. In an embodiment, decision component 907resides on one or more servers 151 in communication with the mobilecommunication device over network 121, i.e., “in the cloud.” Thedecision component may rely on one or more analysis systems, such as theanalysis systems disclosed herein. Because decision component 907resides on computing resources that are more powerful than the mobilecommunication device, it can provide a more robust analysis to determineif data should be considered bad or good for device 101. Furthermore,analysis that takes place on server 151 can take advantage of datacollected by the server to produce an assessment that would not bepossible only relying on data available to mobile communication device101. For example, decision component 907 on server 151 may determinethat a data object is malicious if behavioral data reported by devicesindicate that the data object sends premium-rate SMS messages or dialspremium-rate phone numbers on devices that it is installed on.

In an embodiment, decision component 907 utilizes one or more types ofinternal analysis systems to characterize whether a data object is goodor bad. The decision component 907 is designed to detect securitythreats without specific definitions for the threats being protectedagainst. In other words, decision component 907 may operate as anadditional security component to compensate for any weaknesses fromknown good component 903 or known bad component 905 and to identify newthreats that have not been previously identified.

One will appreciate that there are a number of analysis systems that maybe utilized by decision component 907, including but not limited tosystems that use heuristic algorithms, rule-based or non-rule-basedexpert systems, fuzzy logic systems, neural networks, or other methodsby which systems can classify a data object. As described above, suchsystems may use a variety of data available to decision component 907,including but not limited to distribution data, characterization data,categorization data, trust data, application data, and the like. Forexample, decision component 907 may analyze applications, libraries, orother executables on a mobile communications device. In an example, thedecision component 907 may contain a neural network which analyzescharacteristics of an executable and determines a security assessmentbased on network connection characteristics. Such characteristics may bedetermined based on information contained in the executable file formator as a result of processing the content of the executable file. Inanother example, the decision component 907 may contain an expert-systemwhich analyzes the behavior of an executable through function calls,system calls or actions an executable may take on an operating system.If an executable access sensitive system calls in a way that signifiesmalicious behavior, the system may flag that executable as potentialmalware and action may be taken.

If decision component 907 is located on mobile communication device 101,it may be desirable to update rules or analysis parameters independentlyof updating the executable code powering the decision component. In anembodiment, the decision component 907 contains a virtual machine-baseddecision system by which an executable can be classified by a set ofrules that may be updated independently of the decision componentitself. Such a system is able to add new logic to detect certain newclasses of undesirable applications on the fly without having to updatethe whole decision component. The system may pre-process the executableso that the virtual machine's logic can symbolically reference theexecutable rather than having to process the executable itself.

In an example, the decision component 907 may consider third partyinformation to evaluate data. A person having skill in the art willappreciate that a mobile communication device 101 is capable ofaccessing an application provider, such as Apple's App Store, theAndroid Market, or other software repository or digital distributionplatforms for providing applications available for download andinstallation on the mobile communication device. In an embodiment,server 151 has access to such application providers and can collectinformation about specific applications. For example, server 151 cansearch for and collect user-generated reviews or ratings aboutapplications. An application that has favorable ratings may be deemedsafe while an application with significantly negative ratings may bedeemed undesirable. Because server 151 may also determine trust data fordata objects, the assessment for an application with negative reviewsmay only indicate that the application is undesirable if the applicationhas a low trust rating while an application with a high trust rating andnegative reviews may still be considered desirable by an anti-malwaresystem.

The above examples illustrate how decision component 907 may utilize anumber of analytical methods in order to fully evaluate the threat levelof data received by or transmitted from the mobile communicationsdevice. Other examples may be contemplated without departing from thescope of this disclosure.

One will appreciate that identifying recognizably good data objects andrecognizably bad data objects, such as by mobile communication device101 or server 151, may be performed by a single component rather than byseparate “known good” and “known bad” components. In an embodiment, asingle recognition component performs the functionality of identifyingboth recognizably good and recognizably bad data objects.

In an embodiment, a recognition component utilizes definitions todetermine an assessment for a data object. The recognition componentfirst examines application data for a data object to determine if anydefinitions correspond to the data object. For example, if therecognition component has access to definitions that are hashes of dataobjects' content, a definition that has the same hash as the hash of agiven data object's content is determined to correspond to the dataobject. In another example, if the recognition component accessesdefinitions that contain byte sequence signatures, a definition with abyte sequence contained in a data object's content is determined tocorrespond to the data object. Each definition may be associated with anassessment so that the recognition component can examine applicationdata for a data object to determine a corresponding definition,determine a corresponding assessment for the definition, and thereforeproduce an assessment that corresponds to the data object. For example,the application data for a data object may include identifyinginformation such as the data object's hash, package name, uniqueidentifier, or other application data such as the data object's content.In an embodiment, the definitions used by a recognition componentrepresent known data objects. In this case, when the recognitioncomponent determines if an assessment for a known data objectcorresponds to a data object being analyzed, the data object beinganalyzed and the known data object do not have to be exactly the same.For example, if a first application from a particular developer isdetermined to be undesirable through analysis (e.g., manual analysis,automated analysis), a definition may be created for the firstapplication that matches the first application's package name. If thedeveloper creates a modified application that has the same package nameas the first application and the recognition component encounters themodified application, the definition is determined to correspond to themodified application because the package name in the definition matchesthe modified application's package name. The recognition component thendetermines that the undesirable assessment for the first applicationapplies to the modified application.

For example, a recognition component may access a database ofdefinitions, each definition indicating a hash of a data object'scontent and an indication of whether a data object to which thedefinition corresponds is considered to be good or bad. In anembodiment, the definitions used by one or more recognition componentsoperating on server 151 are stored on server 151 or on data storage 111.In an embodiment, known good component 903 and known bad component 905are each implemented on server 151 using a recognition component. Forexample, a known good component may include a recognition componentwhere all of the definitions accessed by the recognition componentcorrespond to an assessment that a data object is considered to be good.In an embodiment, known good and known bad components are eachimplemented as recognition components that match application data for adata object against known good and known bad application data. Forexample, a known good component may have a list of known good hashidentifiers, package names, and cryptographic signers that it tries tomatch with data objects being analyzed. In an embodiment, if a dataobject has any characteristic in the known good list, it is consideredsafe. In an embodiment, server may use a similar known bad system thatmatches known bad application data to application data for a data objectbeing analyzed. Other known good and known bad analysis systems arepossible without departing from the scope of this disclosure. In anembodiment, the recognition component produces a variety ofassessments—not simply “good” or “bad.” In an embodiment, therecognition component uses a single assessment instead of storingmultiple assessments if all definitions only have a single correspondingassessment, such as in the case where the recognition component onlyidentifies whether a data object is “known bad.” Other variations arealso possible without departing from the scope of this disclosure.

FIG. 12 illustrates an embodiment of this disclosure used to assess dataobjects on a mobile communication device. A mobile communication device101 may first initiate a scan of a data object, such as in the case of afull system scan or when the data object is being executed or installed1201. The recognition component evaluates application data for the dataobject (e.g., package name, hash of data object's content, uniqueidentifier, content of data object) to determine if a definitionaccessible to the recognition component corresponds to the data object(block 1202). For example, as discussed above, the correspondence mayinclude matching identifying information for the data object to datacontained in a definition or matching the data object's content tosequences, patterns, or logic contained in a definition. If a definitioncorresponds to the data object, then the recognition componentdetermines the corresponding assessment for the data object. In anembodiment, recognition component in block 1202 utilizes a data store ofdefinition and assessment information. For example, as discussed above,the definitions stored on the mobile communication device may bepre-populated or populated when the mobile communication device receivesthe definition and assessment information from server 151. In anembodiment, the definitions stored on the mobile communication devicemay be considered a cache, the cache functioning as described above. Ifthe recognition component on the mobile communication device determinesan assessment for the data object (block 1203), that assessment isprocessed to determine how to treat the data object (block 1204). Forexample, if the assessment indicates that the data object is malicious,then the mobile communication device may disallow the data object frombeing executed or prompt the device's user to uninstall the data object.If the recognition component on the mobile communication device does notdetermine an assessment for the data object (block 1203), then mobilecommunication device 101 transmits data object information such asapplication data (e.g., identifying information, content of the dataobject) to server 151 (block 1205). The server receives the data objectinformation (block 1206), and a recognition component on serverevaluates the data object information to determine if a definitionaccessible to the recognition component corresponds to the data object(block 1207). If a definition corresponds to the data object (block1208), then server 151 determines an assessment for the data object andtransmits it to mobile communication device (block 1209). If therecognition component does not determine a corresponding definition orassessment for the data object (block 1208), a decision component on theserver analyzes the data object information (block 1210). If thedecision component produces an assessment, then server 151 transmits theassessment to the mobile communication device (block 1209). If noassessment is produced by the decision component, then the servertransmits an indication that the data object is unknown to the mobilecommunication device (block 1209). Mobile communication device 101receives the assessment from the server (block 1211) and processes theassessment information to determine how to treat the data object (block1204). In an embodiment, mobile communication device 101 addsinformation from the assessment received from server 151 to its localdefinition cache when it processes assessment information (block 1204).For example, the device may store information such as a disposition forthe data object (e.g., “known good”, “known bad”, “malware”, “spyware”),an identifier transmitted by server 151, and definition informationgenerated by the device or transmitted by server 151 (e.g., hash of thedata object's content, data object's package name).

In an embodiment, mobile communication device performs analysis on adata object being scanned using a local decision component on the mobilecommunication device before transmitting data object information toserver 151 in the case where the recognition component on the mobilecommunication device does not determine an assessment. In an embodiment,analysis by the local decision component and transmitting data objectinformation to the server occur in parallel to minimize delay to a user.One skilled in the art that a variety of configurations of thecomponents in a combined client-server anti-malware system are possiblewithout departing from the scope of this disclosure.

In an embodiment, mobile communication device 101 transmitsauthentication information such as authentication credentials or sessioninformation to server 151 whenever sending information about a dataobject so that server can associate information exchanged with aparticular account on the server.

E. Application Assessment and Advisement System

Previous portions of this disclosure described various systems andmethods for collecting different types of data from one or more mobilecommunication devices and other sources as well as analyzing thecollected data to produce assessments for data objects. The following isa discussion of how server 151 can use assessments for display, exposurevia API, and a variety of other purposes. Some examples of assessmentsthat have been disclosed herein include output from one or more analysissystems (e.g., characterization data, categorization data, trust data,and distribution data) and one or more ratings for a data object (e.g.,security rating, privacy rating, battery rating, performance rating,quality rating). One having ordinary skill in the art will appreciatethat assessment information pertains to a wide variety of informationwhich can be used to understand the effects of installing a given dataobject on a mobile communication device beyond a typical anti-malwaresystem's assessment of whether the data object is malicious or not. Inaddition, this assessment information can be used to guide decisionsregarding whether to download and install of different types of dataobjects. Such information can be useful to an individual user trying todecide whether to install a certain application on his mobilecommunication device. Such information can also be useful to an ITadministrator trying to decide whether to deploy a certain applicationto a plurality of mobile communication devices. In an embodiment, a useror IT administrator can use this assessment information for applicationpolicy enforcement.

One having skill in the art will appreciate that the data available toserver 151 and assessments produced by the server are useful beyondanti-malware purposes. For example, the assessments can detail whether adata object is known for excessively draining a mobile communicationdevice's battery or if a data object utilizes an undesirable amount ofnetwork resources. Because server 151 continues to gather, store, andanalyze data to produce assessment information, in an embodiment, server151 can provide information that details how a data object is estimatedto affect a mobile communication device before the data object isinstalled on the mobile communication device. For example, server 151can provide estimated battery usage information and/or network usageinformation for an application.

When users interact with assessments, it may be desirable that theassessments represent an appropriate level of granularity so that usersdo not feel that the assessments are too broad or too narrow. In anembodiment, server 151 merges assessments for multiple data objects intoa single assessment and transmits the merged assessment. For example, ifan application contains multiple data objects (e.g., executable andmultiple libraries), a user may wish to see an assessment for theapplication as a whole, not multiple assessments for its constituentdata objects. Similarly, if there are multiple versions of anapplication (on a single platform or multiple platform) that exhibitsimilar characteristics, an enterprise policy administrator making adecision about the application may only wish to view a single assessmentthat encompasses all versions of the application.

In order to merge assessments for multiple data objects, server 151 mayuse application data such as file paths, version numbers, package names,cryptographic signers, installer source, and other information todetermine that a group of data objects pertain to a particular versionof an application and/or that one or more data objects or group of dataobjects belong to different versions of an application. For example, ifa set of executables are commonly seen in the same directory together,server 151 may determine that those executables are all related to thesame application. In another example, if an application package has botha package name and a version identifier embedded in it, server 151 maydetermine that two data objects with the same package name andhuman-readable application name but different version identifiers aremultiple versions of the same application.

Because it may be desirable for assessments to provide a consistent formof information between platforms, an embodiment of this disclosure isdirected to server 151 including some or all of the same fields inassessments for data objects that run on different platforms. Forexample, even though the location APIs on different smartphone operatingsystems are very different in their function, server 151 may performoperating system specific analysis on data objects to produce across-platform assessment of whether each data object accesses thedevice's location. If the assessment were in the form of a list ofcapabilities for the data object, both a mapping application onBlackBerry and a location-based social network on Android would have the“accesses device location” capability. Similarly, battery usage may becalculated differently on each platform, but server 151 may produce across-platform assessment of the estimated daily battery use measured asa percentage of total battery capacity. In an embodiment, mergedassessments for multiple data objects include information about therange of characteristics and categorization for data objects. Forexample, an assessment may show a trend in the battery usage of multipleversions of an application. An application that used a lot of battery inan old version but has recently decreased its battery usage may beacceptable while an application that has consistently high battery usagemay be unacceptable.

An embodiment of this disclosure is directed toward server 151 makingassessments for data objects available via a web interface. For example,users may wish to be able to learn more about the characteristics andcapabilities of applications they have on their mobile devices. Server151 may expose, as a web interface, an index of applications for whichassessments are available and an assessment for each of theseapplications. In order to facilitate easy location of applications,server 151 may organize applications in a variety of ways, such asalphabetically, by their characteristics, by their categorization, andby platform. In addition, server 151 may allow a user to search forapplications using terms that match the application's name, description,or fields in the application's assessment (e.g., all applications thatrun on Android OS and send location to the internet). Furthermore,publicly displaying assessments may assist in the transparency ofapplications.

For example, application vendors may direct users to the assessment pagegenerated by server 151 as an independent third-party assessment of thecapabilities of an application so that users can verify what theapplication is doing. In an embodiment, server generates a web interfacethat allows a user to view an application's conditional assessment basedon device data (e.g., how much battery does this application use on aMotorola Droid, how much network data does this application use on AT&TWireless) and compare different conditional assessments (e.g., thisapplication's battery usage on a Motorola Droid vs. a HTC Hero, how muchnetwork data does this application use on AT&T Wireless vs. VerizonWireless). Such conditional assessments may be helpful to identifyanomalous behavior in particular circumstances—for example, theassessment page may indicate that a certain set of handsets, operatingsystem versions, or other applications installed on a device cause ahigher error rate or anomalous change in certain assessmentcharacteristics for this application. In an embodiment, server 151identifies data objects having extreme values for particular assessmentvalues. For example, server 151 may generate a web page identifyingwhich applications use more than 1 gigabyte of network data per month orwhich applications use more than 10% of a device's battery.

Because assessment data generated by server 151 may be utilized toprovide a variety of other products and services, an embodiment of thisdisclosure is directed toward server 151 exposing assessment data via anAPI. All functionality exposed by a web interface, as described above,may also be exposed as an API so that a variety of products and servicesmay be built. For example, server 151 may provide an HTTP API by whichsupplying a data object's package name or content hash in the requestURL will result in the server returning an assessment for the dataobject identified by the package name or content hash. In anotherexample, server 151 may generate a JavaScript file that can be includedby a remote web page and displays an interactive assessment view for aparticular data object.

In an embodiment, server 151 can cause assessment data, such as a ratingor disposition as to whether an application is desirable or not, toappear in an application marketplace. One will appreciate thatapplication marketplaces may be implemented in a variety of ways, suchas using a web site, using a mobile client application, using a PC-basedclient application, and using a messaging service such as SMS. As such,rather than subjective user-provided review information, an embodimentof this disclosure will provide objective assessment information for anapplication or other data object.

For example, server 151 may provide an API by which it may be queriedfor assessment data, or server 151 may proactively analyze all of theapplications available in an application marketplace, transmittingassessment data to the marketplace provider. In an embodiment, a usercan search the application marketplace for only those applications thatmeet certain desirable criteria, such as security, privacy, deviceefficiency, trustworthiness, and the like. In an embodiment, applicationproviders can use the aggregated information in order to provide qualitycontrol measures. The application provider may only feature applicationsthat meet certain battery efficiency criteria, a standard for anacceptable number of crashes or errors, certain network trafficlimitations, privacy protections, and the like. In this fashion, anembodiment of this disclosure can improve the offerings on anapplication marketplace, thereby encouraging developers to create betterapplications. In an embodiment, the assessment information may be usedas a certification system, wherein an application meeting certaincriteria may be marked with a symbol, badge or other icon denoting thepositive assessment for the application. For example, applications thathave a high trust rating or applications that only access a minimal setof private information may be considered certified. In order to verifyan application's certification, the certification marker may have a linkor other way for a user to retrieve a full assessment from server 151.

In an embodiment, server 151 transmits assessment information to mobilecommunication device 101 for display. For example, a mobile device mayhave an interface by which a user can explore assessments for allapplications installed on the device. The interface may allow a user toview assessment information for a particular application as well asallow a user to view which applications match a set of assessmentcriteria (e.g., all applications that send the device's location to theinternet, the top 10 battery users, all applications that use more than50 megabytes of network traffic per month). In an embodiment, mobilecommunication device 101 displays an interface as a part of anapplication marketplace, an application download process, or anapplication installation process on a mobile communication device sothat a user browsing an application available for download ordownloading/installing an application sees assessment information forthe application. When browsing, downloading, or installing anapplication, the device transmits identification information to server151 and receives an assessment for the application, displaying some orall of the assessment on a user interface. For example, the interfacemay display the capabilities of the application or characteristics ofthe application. The interface may also be interactive, allowing theuser to explore aspects of the assessment, requesting additionalassessment information from server 151 if necessary. In another example,the device may display an indicator of trust for an application, asdetermined by server 151 and transmitted to device 101 as part of anassessment. The indicator of trust may be displayed in a variety ofways, including as a certification seal (e.g., “Lookout™ certified”) ora rating (e.g., “A+”, “B−”, “C+”).

In some cases, users will not read lengthy security explanations, so itis important to display security information about applications in sucha way that is easily understandable. In an embodiment, a mobilecommunication device 101 displays a graphical assessment indication foran application. For example, notable aspects of assessments may bedisplayed as icons or badges for the application. Some examples includebadges for being “battery efficient”, being a “battery hog”, “accessinglocation”, having “spy capabilities”, being a “social network”, andbeing a “file sharing app”. The badge for each notable assessment mayinclude an illustration making the badge easy to understand andcoloration indicating whether the assessment is merely informational orsomething potentially critical. For example an application beingefficient with battery use may have a green icon showing a full batterywhile an application that typically uses a lot of battery may have a redicon showing an empty battery.

Because server 151 continually gathers information and improvesassessments, assessment information can be updated on applicationmarketplaces and/or mobile communication devices that have cached theassessment information. For example, server 151 may send a notificationto the application marketplace or mobile communication device indicatingthat new assessment information is available. In another example, server151 may simply transmit the updated assessment information so that oldinformation is overwritten.

In addition to viewing assessments on a device for data objects that areinstalled on that device, it may also be desirable to view assessmentsfor data objects installed on a device from a web interface. Forexample, a user may wish to use his or her PC to explore assessments forapplications installed on his or her device. As discussed, in anembodiment, mobile communication device 101 transmits application datafor data objects it has installed to server 151. Because server 151 maystore which applications are currently installed on device 101, theserver can generate a user interface displaying assessments for thoseapplications. For example, server 151 may generate and transmit a webinterface allowing a user to view a list of all applications installedon a device, view an assessment for each installed application, andexplore which installed applications match particular assessment values(e.g., all applications that can access my location). To preventdisclosure of private information, server 151 may require that a userlog in using authentication credentials in order to view assessments forthe applications on his or her device. Furthermore, an enterpriseadministrator may wish to view assessments for a group of devices from acentral management console.

In an embodiment, server 151 generates a web interface that allows auser to view assessments for applications installed on multiple devices.For example, the web interface may allow a user to explore all apps thatare installed on a group of devices that match a certain assessmentfield (e.g., file-sharing applications), view risk rating assessmentsfor the group of devices, view all of the capabilities for applicationsinstalled on the deployment, and determine which devices and which appsare causing certain capabilities and risk exposures. A user may start byusing server 151 to generate an overall set of security, privacy, andbattery risk ratings for the group of devices then click on a rating toview the list of applications most contributing to that risk rating. Auser can then view which devices have a given application. In anotherexample, a user may start by using server 151 to generate a list of allcapabilities for applications installed on the group and then click agiven capability to view all of the applications installed on the groupthat have that capability. From there, the user may further explorewhich devices in the group have a given application installed. In anembodiment, assessments for a group of devices are exposed by server 151in the form of an API for use by external services such as managementconsoles. For example, server 151 may expose risk ratings for the groupof devices to a centralized security reporting system via an HTTP API.

On mobile communication devices, battery and network data are oftenlimited in such a way that applications can adversely affect thedevice's battery life and can cause network use overage charges. Anembodiment of this disclosure is directed to using assessments to makeusers aware of applications' network or battery usage and alert users inthe case of an abusive application. Software on the device retrieves anassessment containing battery and network usage characteristics for anapplication from server 151 and displays the assessment to the user. Asdescribed above, a device requesting assessment information from server151 may include application data for the application. The assessment maybe customized for the particular device the user is using by the devicesending device data when retrieving the assessment or by sendingauthentication data that associates the assessment request withpreviously transmitted device data. For example, the assessment mayindicate that an application will likely reduce a user's model ofphone's battery life by 5% or 1 hour; whereas a different model phonethat has different battery life characteristics may receive anassessment that the same application reduces the phone's battery life by10% or 3 hours. The assessment display may occur as part of an on-deviceapplication marketplace or as a user interface dialog before, during, orafter installation of an application.

Furthermore, after the user installs multiple applications, it may bedesirable for that user to understand which applications are mostcontributing to network usage or battery life based on the applications'actual behavior on the device. In an embodiment, the device collectsbehavioral data for the battery and network usage of an application andallows a user to view the actual behavioral data from an interface onthe device. For example, the interface may allow a user to view aparticular application's battery and network usage as well as view thetop network and battery using applications in order to identify whichapplications are contributing to network overage or short battery life.In an embodiment, mobile communication device 101 reports behavioraldata for applications installed on the device to server 151 and allowthe user to view the actual behavioral data via a web interfacegenerated by the server. One having ordinary skill in the art willappreciate that other characteristics of mobile applications can bemonitored and shown to users as well.

Because a single application can cause significant problems with respectto battery life, network usage, or other limited resources, it may bedesirable to notify a user when an application is behaving undesirably.In an embodiment, mobile communication device 101 monitors the networkand battery usage of applications installed on the device and notifiesthe device's user when an application exceeds desirable limits. Forexample, the user may set thresholds for how much data applications maytransmit and receive before he or she is notified. In another example, auser is notified when the device determines that an application willadversely affect the user's battery life or phone bill. If a usertypically uses a phone for 20 hours before plugging it in and anapplication on the device reduces the estimated battery life to lessthan 20 hours, it's likely that the user will run out of battery. It maythen be important to alert the user that there is an action he or shecan take to avoid running out of battery, namely uninstalling orotherwise disabling high battery using applications.

In an embodiment, in order to prevent applications on a user's devicefrom exceeding the user's data plan, device 101 or server 151 predictsthe future data usage of a device and gathers information about thedevice's data plan. In order to gather information about a device's dataplan, device 101 or server 151 connects to a network operator's serversto determine data plan information such as the data allocation perbilling cycle, what their billing cycle is, and how much data has beenused during the current billing cycle. Communications to the networkoperator's servers may occur in a variety of ways, such as via an HTTPAPI or SMS messaging. If software on a device uses SMS messaging toretrieve a user's data plan information, the software may automaticallyconsume the response message sent by the network operator's servers inorder to prevent the communication from showing up in the user's inbox.In order to predict future data usage, server 151 may analyze typicaldata usage for applications installed on a device and actual data usageon that device. If an application is newly installed, typical data usagemay be used while for an application that has been on the device formonths, actual data usage may be used. If applications on device 101 usenetwork data at a rate that would exceed the device's data planallocation by the end of the billing cycle, software on the devicedisplays an alert indicating the likely overage charges. The alert mayalso display the applications most contributing to the data usage andgive the user to uninstall or reconfigure the applications. Device 101may report the alert to server 151 which may also send a notification(e.g., via email) indicating the potential for data overage. Software ondevice 101 or server 151 may display an indication of the currentpredicted data usage relative to the device's data allocation so that auser may adjust his or her application usage patterns accordingly. Forexample, if a user is worried about exceeding his or her data plan, heor she may check what the current predicted data usage is beforeengaging in a video chat.

Because the applications installed on a device may have a significantimpact on the risk exposure of the device, it may be desirable for auser or administrator to set policy for what applications are desirableto install on a device or group of devices. The following is adiscussion of how protection policy can be implemented on one or moremobile communication devices. In an embodiment, policy includesblacklists and whitelists. A blacklist is a set of applications orassessment criteria that are explicitly denied from running on a mobilecommunication device while a whitelist is a set of applications orassessment criteria that are explicitly allowed to run on a mobilecommunication device. For example, a policy may allow only applicationson a whitelist or only applications not on the blacklist. In anembodiment, explicit application entries have higher priority thanassessment criteria entries. For example, a policy may specify certaincapabilities (e.g., sending a device's location to the internet) thatare blacklisted but specify certain applications that are whitelisted.In this case, all applications that send location to the internet may beblocked unless they are explicitly on the whitelist because the explicitapplications on the whitelist are of higher priority than the assessmentcriteria on the blacklist. One skilled in the art will appreciate that avariety of policy schemes can be implemented without departing from thescope of this disclosure.

Users may have individual preferences for the type of applications theywant on their mobile devices. Some users, for example, may be sensitiveto privacy issues, while other issues may want to optimize their batterylife. In order to allow users to utilize application assessments to gaingreater insight into the applications they use or are considering touse, an embodiment of this disclosure is directed to software on amobile communication device allowing a user to set policies based onassessment criteria for applications, the software blocking applicationsthat exceed an undesirability threshold. When a user attempts to installan application, the software requests an assessment for the applicationfrom server 151 and receives the assessment from the server.

For example, if the user attempts to install an application that has thecapability of sending location information to the internet but has apolicy to disallow any applications that can send his or her location tothe internet, then software on the mobile communication device willblock the installation. In another example, a user may set privacy,security, and battery life policy thresholds individually on a relativescale (e.g., 0 to 10). When the user installs an application, softwareon the device retrieves an assessment for the application and comparesthe application's privacy, security, and battery ratings with the policythresholds and alerts the user if the application exceeds the configuredpolicy. Instead of blocking installation of an application that isundesirable, a user may want to simply be warned of the undesirability.

In an embodiment, the user can ignore the alert and choose to accept theapplication anyway. In an embodiment, the device displays a userinterface indicating that an application is undesirable for the user.For example, a mobile device may display an indication of whether anapplication being viewed for possible download in an applicationmarketplace meets the user's desirability criteria. In another example,software on a device may allow a user to view all applications that donot meet desirability criteria. Such an interface may be useful if auser changes his or her criteria and wants to view applications that arenow undesirable given the new criteria.

IT administrators, parents, network operators or other peopleresponsible for multiple mobile communication devices may wish to setpolicy on multiple mobile communication devices without physical accessto all of the devices. In an embodiment, server 151 allows a user oradministrator to set policy for a device or group of devices. When adevice 101 attempts to install an application, the device sends arequest to server 151 for an assessment of the application. Based onpolicy configured on server 151, the assessment contains an indicationof whether the application is allowed or disallowed and may also containthe policy criteria for why a disallowed application was assessed to bedisallowed. In an example, policy on server 151 is configurable via aweb interface.

In an embodiment, server 151 allows policy to be configured byassessment criteria as well as on a per application basis. For example,an administrator may use server 151 to block all applications that arein a certain category such as social networking applications or allapplications that access certain capabilities such as the ability totransmit files or other sensitive data from a device. In an example, anadministrator may wish to only allow particular applications by creatinga whitelist, blocking all applications not on the whitelist. In afurther example, an administrator may permit all applications other thanparticular applications that are on a blacklist because they are knownto be undesirable. Because the set of applications allowed or deniedunder a policy may be pre-computed, an embodiment of this disclosure isdirected to server 151 generating a set of policy definitions andtransmitting the policy definitions to one or more mobile communicationdevices 101. For example, if a group of devices has a policy to onlyallow applications that are on a whitelist, server 151 may transmit alist of identifying information for the whitelisted applications to amobile device so that the device does not need to contact the server forassessments every time it encounters an application.

When configuring policy using abstract concepts such as applicationcategorization and capabilities, it may be desirable for a user oradministrator to see what applications would be allowed/denied orwhether a particular application would be allowed/denied ifconfiguration changes were to be made. In an embodiment, the policyconfiguration user interface on mobile communication device 101 orserver 151 includes an interface for viewing applications that would beblocked or allowed as part of a configuration change. If theconfiguration change interface is displayed on mobile communicationdevice 101, the device may send requests for data to server 151 topopulate the interface. It may be desirable to show all of theapplications allowed or blocked after the configuration change goes intoeffect or only the difference in applications allowed or blocked betweenthe current configuration and the new configuration. Because the numberof applications affected by a configuration change may be very large,the interface may display summary information and allow a user to searchfor a particular application to determine whether the configurationchange affects that application and whether the configuration changewould result in that application being allowed or blocked. In anembodiment, the interface displaying the effect of a configurationchange indicates whether any popular applications would be blocked. Forexample, application popularity may be determined based on overalldistribution data determined by server 151 or by the prevalence of theapplication in the group of devices being managed. In an embodiment, thechange result interface only displays changes that affect applicationsthat are currently installed on at least one device in the group beingmanaged.

In order to prevent a policy system from interfering with acceptableusage of mobile communication devices, an embodiment of this disclosureis directed to server 151 maintaining sets of acceptable apps andallowing a user or IT administrator to easily add those sets to awhitelist, the whitelist automatically including changes to the sets ofacceptable apps. For example, server 151 may maintain a list ofapplications that are popular overall or a list of popular applicationsby application category. In a policy configuration interface, the servermay present a way to include all popular applications or only popularapplications in particular categories (e.g., games, social networks) inthe policy's whitelist. In an embodiment, such dynamic list policies areof higher priority than assessment criteria entries on blacklists andwhitelists but of lower priority than explicit application entries. Inanother example, server 151 may maintain a list of applications withhigh trust. In a policy configuration interface, the server may presenta way to include all high-trust applications in the policy's whitelist.Whenever the high-trust list is updated, applications with high trustare effectively considered whitelisted when making policy assessments.

Because a mobile device deployment may already have a device managementserver or service in place, it may be desirable for server 151 to supplydata to a device management server that actually performs the policyenforcement. In an embodiment, server 151 interfaces with a devicemanagement server to configure application policy on the devicemanagement server. For example, the device management server may supportconfigurable application blacklists and whitelists. If a user setsconfiguration on server 151 to only allow applications that are on awhitelist or that match certain assessment criteria, server 151generates the list of applications to be whitelisted and transmits thelist of applications to the device management server in a format andover a protocol that the device management server supports. Similarly,if a user configures a blacklist on server 151, the server generates thelist of applications that are on the blacklist and configures the devicemanagement server to enforce the blacklist. In an embodiment, server iscapable of configuring multiple device management servers. For example,if an organization supports multiple mobile device operating systems anduses different mobile device management servers, an administrator canconfigure a cross-platform policy on server 151 (e.g., blocking all filesharing applications). Server 151 may then identify all of theapplications across multiple platforms whose assessments match thepolicy and configure the appropriate application policies on devicemanagement servers. Because each device management server may onlysupport a subset of mobile device platforms that server 151 supports,server 151 only transmits policy information to a device managementserver that corresponds to data objects that run on operating systemsthat are supported by the device management server. For example, if adevice management server only supports Blackberry devices, server 151may only configure the device management server's blacklist and/orwhitelist with information about Blackberry applications.

In an embodiment, policy compliance checking can be performed by eitherserver 151 or mobile communication device 101. For example, if serverperforms compliance checking, any compliance settings are stored onserver 151 so that any configuration performed on mobile communicationdevice 101 results in that configuration being transmitted to theserver. When the device requests an assessment for an application fromserver 151, the server includes in the assessment an indication ofwhether the application is allowed or disallowed by policy. In anotherexample, if mobile communication device 101 performs compliancechecking, any compliance settings are stored on mobile communicationdevice 101 so that any configuration performed on server 151 results inthat configuration being transmitted to the device. When the devicereceives an assessment for an application, it compares the assessment tothe policy configuration to determine if the application is allowed.

In an embodiment, policy management is integrated with a server-coupledanti-malware system so that signatures and assessments for applicationsprovided by server 151 enable device 101 to block data objects thatviolate policy. For example, when a device 101 requests for anassessment from server 151, the server's assessment indicates that anapplication is undesirable if the application is considered malicious orif it violates policy. In either case, the assessment produced mayindicate further information about why the application was found to bemalicious or policy-violating. In another example, server 151 maypre-emptively transmit signatures for malicious or policy-violatingapplications to mobile communication device 101 so that the device canrecognize whether a data object is desirable or undesirable withouthaving to contact server 151.

If a device 101 has installed an application that violates a protectionpolicy in place on either the device or server 151 or the assessment foran application has been updated to make it violate the protectionpolicy, it may be desirable for remediation actions to be taken by thedevice or other systems. In an embodiment, if a device has anapplication installed that violates the protection policy for thatdevice, the server or software on the device can enact remediationactions to occur. Depending on whether policy compliance is determinedat the device 151 or server 101, either the device or server maydetermine what remediation actions to take.

For example, if a user installs an application and the assessmentreceived from server 151 indicates that the application is acceptablebut at some point in the future server determines that the applicationis unacceptable, server 151 transmits an updated assessment to thedevice including remediation actions for the device to take. In anotherexample, if a user installs an application on a device and the devicereceives an assessment from server 151 indicating that the applicationis acceptable but software on the device gathers behavioral data thatshows that the application violates policy (e.g., the applicationattempts to acquire the user's location), the device may undertakepre-configured remediation actions such as removing the application. Thedevice may also transmit this behavioral data to server 151 and indicatethe policy violation. One skilled in the art will appreciate that usingbehavioral data to enforce policy can protect mobile communicationdevice in a variety of situations such as when a vulnerability in anapplication is exploited, when an application only behaves undesirablyon a subset of devices (e.g., a targeted attack against employees of aparticular company), or when an application only behaves undesirablyafter a period of time (i.e. a time bomb).

When a device is detected to be violating policy, a variety ofremediation actions are possible, for example, any violatingapplications may have their processes ended, may be uninstalled orisolated from accessing certain system functionality (e.g., internet,private data), or may be restricted from accessing certain networks(e.g., only allowed to access Wi-Fi, not the cellular network). It mayalso be desirable to isolate the whole device from accessing sensitiveresources such as a corporate email or VPN server while it is out ofcompliance to prevent information leakage. Other remediation actions mayinclude those disclosed in U.S. patent application Ser. No. 12/255,614,filed on Oct. 21, 2008 and incorporated in full herein.

If an administrator is able to set policy using server 151, it may alsobe desirable for a user to use server 151 to view the compliance statusof devices that the policy applies to. In an embodiment, server 151determines whether a group of mobile communication devices is incompliance with application policy and which applications are installedon devices in the group. For example, if mobile communication devicesreport the applications they have installed and server 151 containspolicy configuration, the server can determine which devices currentlyviolate the policy set by an administrator. To allow an administrator toview the compliance status, server 151 may generate a web interfacelisting whether or not all devices are in compliance and if any devicesare out of compliance, how many there are. The interface may also allowthe administrator to view specific devices that are out of compliance,view which applications make the devices out of compliance, and initiateremediation actions (e.g., removing an application) remotely.

In an embodiment, server 151 presents a one-click remediation actionwhereby an administrator can click a single button to remotely initiateremediation actions on all devices in the group the administrator ismanaging. For example, if an administrator managed 100 devices and 10 ofthe devices had applications that violated policy, the administratorcould click the one-click remediation button on the web interface tocause the server to send indications to each of the 10 out-of-compliancedevices to remove the undesirable applications without any userintervention required. Once the remediation actions completed, eachdevice 101 may send an indication to server 151 indicating whether itwas successful or not. During the remediation process, server 151 maygenerate an interface by which the administrator can view the status ofthe remediation. Other methods of server exposing compliance statusinclude server 151 exposing an API (e.g., for use by a securitymanagement console) and server 151 generating reports that can bedownloaded.

In some cases, it may be desirable for a user or administrator toreceive a notification if he or she installs an application that isconsidered undesirable or if a previously installed application is newlyconsidered to be undesirable based on an updated assessment. In anembodiment, mobile communication device 101 transmits information aboutthe installation of a data object to server 151. If server 151determines the data object to be undesirable based on universalundesirability characteristics or characteristics for the user, theserver transmits a notification. For example, if a user installs anapplication that is assessed as desirable, but at some point in thefuture, the application begins to exhibit malicious or other undesirablebehavior such as wasting battery, the server may change its assessmentto indicate that the application is undesirable. The notification maytake a variety of forms, such as an email, SMS message, or userinterface dialog displayed on a web page, on a PC, or on a mobilecommunication device.

For an IT administrator managing a plurality of mobile communicationdevices, policies can be set for a specific application, even if theapplication is available on multiple platforms and has multipleversions. For example, it is not uncommon for an IT administrator tomanage a fleet of mobile communication devices running differentoperating systems. The fleet of mobile communication devices can includeiPhones, BlackBerry devices and Android devices. However, if a certainapplication is known to be undesirable on all three device operatingsystems, such as a social networking application that can discloseprivate information, then the IT administrator can block all versions ofthe application from installation, regardless of platform. However, ifan application can share sensitive information on one platform but notothers, then the IT administrator can allow installation of theapplication on only the platforms that don't share sensitiveinformation. As discussed above, it may also be desirable for an ITadministrator to make policy decisions about all versions of anapplication at once instead of having to maintain a policy that treatsmultiple versions of an application as separate decisions. Because thereare some applications that are updated very frequently, it would quicklybecome a very difficult task to manage application policy if anadministrator could not treat all versions of a particular applicationas one policy decision.

Because an application may drastically change between updates, it'sdesirable for an administrator to be aware of any changes that couldaffect the administrator's decision of whether or not to allow theapplication. An embodiment of this disclosure is directed to server 151sending a notification in the case of an application that is present ona blacklist or whitelist changing its capabilities or characteristicssignificantly. For example, if a new version of an application that ison an administrator's whitelist has the capability to transmit filesfrom a user's device while previous versions did not, then server 151may send an email or text message to the administrator indicating thechange. The policy management interface on server 151 may also display alist of applications that may need attention based on changedcharacteristics.

In order to simplify configuration, an embodiment of this disclosure isdirected to software on mobile communication device 101 or server 151may provide default policies that account for common use cases. Forexample, a user may be able to select that they are concerned withbattery life and location privacy but they are not concerned withnetwork usage and phone number privacy. By selecting such concerns, thedevice or server automatically configures policies and thresholds forundesirable applications. In an embodiment, server 151 or device 101contains pre-set policies for compliance with regulations. For example,financial industry or healthcare industry workers may be required tohave a particular set of application policies in place to prevent thedisclosure of sensitive information. Because the set of applicationsallowed or denied under these regulations may change over time, server151 may automatically update the specific policy decisions that enforcethe regulation without an administrator needing to specificallyconfigure them. In order to allow for inspection and auditing, server151 may generate a list of policy decisions it is employing to complywith regulation and may notify an administrator when policy decisionswill change. If an administrator rejects certain policy decisions, he orshe may override the default policy set by server 151.

As it may be desirable to simplify the policy configuration process, anembodiment of this disclosure is directed to server 151 or mobilecommunication device 101 presenting a series of questions to a user oradministrator, the answers to the questions being used to automaticallyset policy. For example, when a user is first setting up applicationpolicy software on his or her device, the software may ask whether theuser has an unlimited data plan, whether the user wants to allowservices to access the device's location, and whether the user wants toblock all tools that can be used to spy on the device. Based on theanswers to the questions the device may set policy of whether to blockhigh data usage applications, whether to alert the user in the case of ahigh data usage application, whether to block applications that send auser's location to the internet, and whether to block espionageapplications. After this initial setup, a user may desire to tweakpolicy decisions, while other users may accept the automaticallyconfigured policy.

Because abusive applications may have a substantially negative impact onwireless networks, an embodiment of this disclosure is directed toproviding “early-warning” information about potentially abusiveapplications. In an embodiment, server 151 may use information such asbehavioral data and other data available to it in order to produce anassessment of whether an application has network access characteristicsthat may be harmful for mobile networks. For example, an applicationthat receives or transmits a large amount of data, sends a large numberof SMS messages, or opens a large number of persistent connections mayadversely affect a mobile network's performance. After assessing anapplication to determine if it is potentially harmful to a mobilenetwork, server 151 stores the assessment. In an embodiment, server 151notifies an administrator when a potentially harmful application isidentified. For example, the notification may be in the form of an emailor text message that contains information about the potentially harmfuldata object.

In an embodiment, server 151 generates a web interface that displaysapplications that have been assessed as potentially harmful to a mobilenetwork. The web interface may be designed to support a review workflowso that potentially harmful applications can be further analyzed by anadministrator. After examining an application, the administrator maywant to take remediation action in some cases while, in other cases, theadministrator may want to take no action. If an administrator chooses totake no action, the application will not be considered potentiallyharmful unless its behavior significantly changes, triggering server 151to identify the application for re-review. In order to prevent multipledata objects for a given application being repeatedly identified aspotentially harmful, if an administrator chooses to ignore anapplication, all versions of that application will also be ignored, asserver 151 can determine whether multiple data objects belong to thesame application or other grouping.

If an administrator is aware of a potentially harmful application, he orshe can take preemptive measures to avoid serious problems if theapplication is installed on more devices. In an embodiment, server 151generates a web interface allowing an administrator to take remediationactions for an application that is considered harmful. A variety ofremediation actions are possible. For example, server 151 may present aninterface allowing the network administrator to communicate with thepublisher of the application and work through a resolution for theharmful behavior. Server 151 may extract the publisher's email addressfrom marketpalce data and allow a network administrator to type in amessage via the server's web interface that server 151 sends to thepublisher. When server 151 sends the email, the reply-to address in theoutgoing email is specially set so that when the publisher responds,server associates the response with the initial message and publishesthe response in the web interface for administrator to view andpotentially continue the conversation. In an embodiment, server 151generates a web interface allowing an administrator to configuresecurity software installed on a group of devices. For example, theadministrator may wish to configure the security software to block thepotentially harmful application or isolate the application so that itcannot communicate via a cellular network. If the administrator desiresto block the application, server 151 may use a variety of mechanisms,such as those disclosed herein to block the application from beinginstalled on devices or to remove the application if it is alreadyinstalled on devices. Because server 151 can identify multiple dataobjects that correspond to the same application, if an administratorblocks an application, all data objects for the application areconsidered to be blocked. If an application that was potentially harmfulis fixed in a subsequent version, server 151 may allow the administratorto specify a range of versions of the application to block.

Because it may be desirable to prevent the download of undesirableapplications, an embodiment of this disclosure is directed to server 151generating network infrastructure configuration data. For example,server 151 may store a set of blacklisted data objects and be able togenerate a set of intrusion prevention system or HTTP proxy rules. Therules may attempt to match identifiers used by mobile devices todownload data objects from an application marketplace or to identify thecontent of undesirable data objects as they are transmitted across anetwork.

In an embodiment, server 151 generates network infrastructureconfiguration data to block network traffic associated with undesirableapplications. Server 151 generates network infrastructure configurationrules that prevent network communication associated with undesirableapplications by server 151 using behavioral data for an undesirableapplication to characterize the network communications associated withthe application and generating rules that block similar network traffic(e.g., traffic to the same IP address, subnet, or hostname). In order toprevent legitimate traffic from being blocked, server 151 may analyzehow unique the undesirable application's network traffic is relative todesirable applications and only block network traffic that is particularto the undesirable application. For example, if an applicationcommunicates with two servers, one which is a well-known server used bya variety of legitimate applications and another which is an unknownserver only communicated with by this application, server 151 wouldtreat the unknown server as particular to the undesirable application.

After determining the appropriate network traffic to block, server 151generates firewall or other network configuration rules to blockundesirable applications' network traffic. For example, if a maliciousapplication is using a particular server to exfiltrate sensitive datafrom peoples' phones, behavioral data for the application may indicatethe IP address, port, and protocol used to transmit the sensitive data.When an administrator wishes to block the malicious application'scapability to steal data, he or she may see the list of servers theapplication communicates with and how many other applications known toserver 151 typically communicate with that server. The administratorthen has the ability to choose which servers to block. After selectingthe servers to block, server 151 generates rules that block the networktraffic. In an embodiment, sever 151 makes configuration data, such asSnort® intrusion detection and prevention system rules, available fordownload via a web interface. In an embodiment, server 151 is configuredto directly connect with a network infrastructure management system todeploy configuration data.

Because an administrator may be primarily concerned with a particularnetwork, an embodiment of this disclosure is directed to server 151producing both aggregate assessments and operator-specific assessmentsto identify potentially harmful applications and generating a userinterface containing both. For example, if an application misbehavesonly when running on a device connected to a particular type of mobilenetwork, the aggregate behavioral data may be within normal bounds;however, the behavioral data for a particular network may be harmful. Anetwork administrator may want to view the behavior of an application onthe type of network he or she is administrating. Because individualmobile networks may treat different behavior as abusive, a user onserver 151 can configure the criteria for considering an applicationharmful to the network.

In the description above and throughout, numerous specific details areset forth in order to provide a thorough understanding of thedisclosure. It will be evident, however, to one of ordinary skill in theart, that the disclosure may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form to facilitate explanation. The description of thepreferred an embodiment is not intended to limit the scope of the claimsappended hereto. Further, in the methods disclosed herein, various stepsare disclosed illustrating some of the functions of the disclosure. Onewill appreciate that these steps are merely exemplary and are not meantto be limiting in any way. Other steps and functions may be contemplatedwithout departing from this disclosure.

What is claimed is:
 1. A method comprising: on a server in communicationwith a plurality of mobile communication devices, the pluralityconnected to a mobile communication device network, receiving, at theserver, first behavioral data associated with monitoring a data objectaccessed by a first mobile communication device of the plurality ofmobile communication devices; receiving, at the server, secondbehavioral data associated with monitoring a copy of the data objectaccessed by a second mobile communication device, different from thefirst mobile communication device, of the plurality of mobilecommunication devices; storing in a data store accessible by the server,the first behavioral data for the data object and the second behavioraldata for the copy of the data object; applying, at the server, a modelto at least some of the stored first behavioral data to determinewhether or not accessing the data object: would have an adverse effecton at least one of the plurality of mobile communication devices, orwould violate an application policy by exceeding a network resourcelimitation; based on the application of the model to at least some ofthe first behavioral data, determining, at the server, that accessingthe data object: would not have an adverse effect on any of theplurality of mobile communication devices, and would not violate theapplication policy; after the step of determining that accessing thedata object would not have an adverse effect on any of the plurality ofmobile communication device and would not violate the applicationpolicy, analyzing the second behavioral data to determine whetheraccessing the data object would: have an adverse effect on any of theplurality of mobile communication devices, or violate the applicationpolicy; based on the analysis of the second behavioral data,determining, at the server, that accessing the data object would nothave an adverse effect on any of the plurality of mobile communicationdevices, and would violate the application policy by exceeding thenetwork resource limitation; creating disposition information includingthe determination that accessing the data object would violate theapplication policy; aggregating disposition information, in a data storeaccessible by the server; and, transmitting, from the server, anotification to a subscriber about aggregated disposition informationthat includes an overall assessment the data object violates theapplication policy.
 2. The method of claim 1, wherein the storedbehavioral data is correlated to mobile communication device data for atleast one mobile communication device that accessed the data object. 3.The method of claim 2, wherein the aggregated disposition information isaccessible to the subscriber through a data feed.
 4. The method of claim2, wherein the notification comprises an email message to thesubscriber, the email message including the aggregated dispositioninformation.
 5. The method of claim 2, wherein the notificationcomprises a text message to the subscriber, the text message includingthe disposition information.
 6. The method of claim 2, wherein thenotification comprises a web interface for the subscriber to access thedisposition information.
 7. The method of claim 1, further comprising:when the disposition information includes a determination that accessingthe data object would violate the application policy, preventing thefirst and second mobile communication devices from accessing the dataobject by generating device configuration information at the server andtransmitting it to the mobile communication devices.
 8. The method ofclaim 1, further comprising: when the disposition information includes adetermination that accessing the data object would violate theapplication policy, preventing the data object from accessing the mobilecommunication device network by generating network configurationinformation at the server and transmitting it to the mobilecommunication device network.
 9. A method comprising: on a server incommunication with a plurality of mobile communication devices, theplurality connected to a mobile communication device network, receivinga first plurality of behavioral data associated with monitoringinstances of a data object accessed by a first subset of mobilecommunication devices; storing in a data store accessible by the serverthe first plurality of behavioral data for the data object; analyzing,on the server, the first plurality of behavioral data, and a number ofmobile communication devices in the first subset of mobile communicationdevices to determine whether accessing the data object would have anadverse effect on any device of the plurality of mobile communicationdevices, or whether accessing the data object by an accessing subset ofmobile communication devices from the plurality of mobile communicationdevices would violate an application policy by exceeding a networkresource limitation; based on the analysis of the first plurality ofbehavioral data, and the number of mobile communication devices in thefirst subset, determining that accessing the data object would not havean adverse effect on any device of the plurality of mobile communicationdevices and would not violate the application policy; after the step ofdetermining no adverse effect on any device of the plurality and noviolation of the application policy, receiving a second plurality ofbehavioral data associated with monitoring instances of the data objectaccessed by a second subset of mobile communication devices; storing inthe data store the second plurality of behavioral data; analyzing, onthe server, the second plurality of behavioral data, and a number ofmobile communication devices in the second subset to determine whetheraccessing the data object by the accessing subset of mobilecommunication devices would violate the application policy by exceedingthe network resource limitation, or have an adverse effect on any deviceof the plurality of mobile communication devices; based on the analysisof the second plurality of behavioral data, the number of mobilecommunication devices in the first subset, and the number of mobilecommunication devices in the second subset, determining that accessingthe data object by the accessing subset of mobile communication deviceswould violate the application policy by exceeding the network resourcelimitation and would not have an adverse effect on any device of theplurality of mobile communication devices; creating dispositioninformation including the determination that accessing the data objectviolates the application policy; storing in a data store accessible bythe server, the disposition information; and on the server, notifying atleast one subscriber of the disposition information that includes thedetermination that accessing the data object violates the applicationpolicy.
 10. The method of claim 9, wherein the first plurality ofbehavioral data is correlated to mobile communication device data for atleast one mobile communication device of the first subset of mobilecommunication devices that accessed the data object.
 11. The method ofclaim 10, wherein the disposition information is accessible to the atleast one subscriber through a data feed.
 12. The method of claim 10,wherein notifying the at least one subscriber comprises transmitting anemail message to the at least one subscriber, the email messageincluding the disposition information.
 13. The method of claim 10,wherein notifying the at least one subscriber comprises transmitting atext message to the at least one subscriber, the text message includingthe disposition information.
 14. The method of claim 10, whereinnotifying the at least one subscriber comprises generating a webinterface for the at least one subscriber to access the dispositioninformation.
 15. The method of claim 9, further comprising: preventingat least one mobile communication device from accessing the data objectby generating device configuration information at the server andtransmitting it to the mobile communication device.
 16. The method ofclaim 9, further comprising: preventing the data object from accessingthe mobile communication device network by generating networkconfiguration information at the server and transmitting it to themobile communication device network.